require 'torch'

require 'nn'

require 'optim'

require 'model'

include('util/auRoc.lua')

require 'lfs'

local cmd = torch.CmdLine()

-- GPU

cmd:option('-gpu', 1) -- set to 0 if no GPU

-- Dataset options

cmd:option('-data_root', 'data') -- data root directory

cmd:option('-dataset', 'deepbind') -- dataset

cmd:option('-seq_length', 101) --length of DNA sequences

cmd:option('-TF', 'ATF1_K562_ATF1_-06-325-_Harvard') -- change for different TF

cmd:option('-alphabet', 'ACGT')

cmd:option('-size', 0) -- how much of each dataset to load. 0 = full

cmd:option('-batch_size', 1)

cmd:option('-class_labels','1,0') --specify positive label first

local opt = cmd:parse(arg)

opt.class_labels_table = opt.class_labels:split(',')

opt.num_classes = #opt.class_labels_table

opt.alphabet_size = #opt.alphabet

local data_dir = opt.data_root..'/'..opt.dataset..'/'

-- Set up GPU stuff

local dtype = 'torch.FloatTensor'

if opt.gpu > 0  then

  collectgarbage()

  require 'cutorch'

  require 'cunn'

  cutorch.setDevice(opt.gpu )

  dtype = 'torch.CudaTensor'

  print(string.format('Running with CUDA on GPU %d', opt.gpu))

else

  print 'Running in CPU mode'

end

local data_dir = opt.data_root..'/'..opt.dataset..'/'

opt.TF = TF or opt.TF

opt.data_dir = data_dir..opt.TF

-- specify directories

model_root = 'models'

data_root = 'data/deepbind/'

viz_dir = 'visualization_results/'

-- ****************************************************************** --

-- ****************** CHANGE THESE FIELDS *************************** --

TFs = {'ATF1_K562_ATF1_-06-325-_Harvard'}

cnn_model_name = 'model=CNN,cnn_size=128,cnn_filters=9-5-3,dropout=0.5,learning_rate=0.01,batch_size=256'

rnn_model_name = 'model=RNN,rnn_size=32,rnn_layers=1,dropout=0.5,learning_rate=0.01,batch_size=256'

cnnrnn_model_name = 'model=CNN-RNN,cnn_size=128,cnn_filter=9,rnn_size=32,rnn_layers=1,dropout=0.5,learning_rate=0.01,batch_size=256'

model_names = {rnn_model_name,cnn_model_name,cnnrnn_model_name}

-- which sequences in the test set to show saliency map for

start_seq = 1

end_seq = start_seq + 0

-- ****************************************************************** --

-- ****************************************************************** --

alphabet = opt.alphabet

rev_dictionary = {}

dictionary = {}

for i = 1,#alphabet do

  rev_dictionary[i] = alphabet:sub(i,i)

  dictionary[alphabet:sub(i,i)] = i

end

OneHot = OneHot(#alphabet):type(dtype)

crit = nn.ClassNLLCriterion():type(dtype)

for _,TF in pairs(TFs) do

  print(TF)

  save_path = viz_dir..TF..'/'

  os.execute('mkdir '..save_path..' > /dev/null 2>&1')

  -- os.execute('rm '..save_path..'/*.csv > /dev/null 2>&1')

  -- os.execute('rm '..save_path..'*.png > /dev/null 2>&1')

  require('data')

  data = {}

  test_seqs = createDatasetOneHot("test", opt)

  -- Load Models

  models = {}

  for _,model_name in pairs(model_names) do

    load_path = model_root..'/'..model_name..'/'..TF..'/'

    print(load_path)

    model = torch.load(load_path..'best_model.t7')

    model:evaluate()

    model.model:type(dtype)

    models[model_name] = model

  end

  for t = start_seq,end_seq do

    print('test sequence number '..t)

    x = test_seqs.inputs[t]:type(dtype)

    X = OneHot:forward(x)

    y = test_seqs.labels[t]:type(dtype)

    --####################### CREATE SEQ LOGO ###############################--

    s2l_filename = save_path..'sequence_'..tostring(t)..'.txt'

    f = io.open(s2l_filename, 'w')

    print(s2l_filename)

    f:write('PO ')

    alphabet:gsub(".",function(c) f:write(tostring(c)..' ') end)

    f:write('\n')

    for i=1,X[1]:size(1) do

      f:write(tostring(i)..' ')

      for j=1,X[1]:size(2) do

        f:write(tostring(X[1][i][j])..' ')

      end

      f:write('\n')

    end

    f:close()

    cmd = "weblogo -D transfac -F png -o "..save_path.."sequence_"..t..".png --errorbars NO --show-xaxis NO --show-yaxis NO -A dna --composition none -n 101 --color '#00CC00' 'A' 'A' --color '#0000CC' 'C' 'C' --color '#FFB300' 'G' 'G' --color '#CC0000' 'T' 'T' < "..s2l_filename

    os.execute(cmd)

    for model_name, model in pairs(models) do

      print(model_name)

      out_file = io.open(save_path..model_name..'_saliency'..t..'.csv', 'w')

      model:resetStates()

      model:zeroGradParameters()

      ------------------------SALIENCY------------------------

      model.model:remove(1)

      output = model:forward(X)

      loss = crit:forward(output, y)

      df_do = crit:backward(output, y)

      inputGrads = model:backward(X, df_do)

      inputGrads = torch.abs(inputGrads)

      inputGrads = torch.cmul(inputGrads,X)

      -- inputGrads = inputGrads:max(2)

      inputGrads = inputGrads:view(opt.seq_length,4)

      score = output[1]:exp()[1]

      print(model_name..': '..tostring(score))

      -- write to output file

      for i = 1,inputGrads:size(1) do

        out_file:write(rev_dictionary[x[1][i]]..',')

        for j = 1,inputGrads:size(2) do

          out_file:write(inputGrads[i][j]..',')

        end

        out_file:write('\n')

      end

      out_file:write('\n')

      out_file:close()

      ---------------- Create visualization----------

      cmd = 'Rscript ./heatmap_scripts/heatmap_saliency.R '..save_path..model_name..'_saliency'..t..'.csv '..save_path..model_name..'_saliency'..t..'.png -25'

      os.execute(cmd..' > /dev/null 2>&1')

    end -- loop through models

    collectgarbage()

  end -- test sequences

  print('')

  print(lfs.currentdir()..'/'..save_path)

  os.execute('rm '..save_path..'/*.csv > /dev/null 2>&1')

  os.execute('rm '..save_path..'/*.txt > /dev/null 2>&1')

end -- TFs