ingranalyze.py

#!python
# Copyright (c) 2014, Sven Thiele <sthiele78@gmail.com>
#
# This file is part of ingranalyze.
#
# ingranalyze is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# ingranalyze is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with ingranalyze.  If not, see <http://www.gnu.org/licenses/>.
# -*- coding: utf-8 -*-
import argparse
from pyasp.asp import *
from __ingranalyze__ import query, utils, bioquali

if __name__ == '__main__':


    parser = argparse.ArgumentParser()
    parser.add_argument("networkfile",
                        help="influence graph in bioquali format")
    parser.add_argument("observationfile",
                        help="observations in bioquali format")

    parser.add_argument('--mics',
                        help="compute minimal inconsistent cores",
                        action="store_true")

    parser.add_argument('--repair', type=int, choices=[1, 2, 3, 4, 5,], default=3,
                        help="choose repair method: 1 flip observed variations, 2 flip influences, 3 define network nodes as inputs,  4 define network nodes as input in an experiment (use only in case of multiple experiments), 5 add influences. default is 3")

    parser.add_argument('--list_repairs',
                        help="compute all minimal repair sets",
                        action="store_true")

    args       = parser.parse_args()
    net_string = args.networkfile
    obs_string = args.observationfile

    print('\nReading network',net_string, '... ',end='')
    net = bioquali.readGraph(net_string)
    print('done.')
    #net.to_file("net.lp")

    print('\nReading observations',obs_string, '... ',end='')
    mu = bioquali.readProfile(obs_string)
    print('done.')
    print('\nComputing input nodes ...',end='')
    inputs = query.guess_inputs(net)
    print('done.')

    print('\nTesting empty network for consistency ... ',end='')
    consistent = query.is_consistent(net)
    print('done.')

    if consistent: print('   The empty network is consistent.')
    else:
      print('   The empty network is inconsistent.')

      empty_net = TermSet(net.union(inputs))
      print('\nTesting empty network plus input nodes for consistency ... ',end='')
      consistent = query.is_consistent(empty_net)
      print('done.')
      if consistent: print('   The empty network is consistent.')
      else:
        print('   The empty network is still inconsistent.')

        if args.mics:
          print('\nComputing minimal inconsistent cores (mic\'s) ... ',end='')
          mics = query.get_minimal_inconsistent_cores(empty_net)
          print('done.\n')
          count  = 1
          oldmic = 0
          for mic in mics:
              if oldmic != mic:
                print('mic ',str(count),':',sep='')
                utils.print_mic(mic.to_list(),net.to_list(),[])
                count += 1
                oldmic = mic

        repair_options= TermSet()
        print('\nCompute repair options, ',end='')
        if args.repair==1:
          print('repair mode: flip observed variations ... ',end='')
          repair_options = query.get_repair_options_flip_obs(empty_net)
          print('done.')
        if args.repair==2:
          print('repair mode: flip influences ... ',end='')
          repair_options = query.get_repair_options_flip_edge(empty_net)
          print('done.')
        if args.repair==3:
          print('repair mode: define network nodes as inputs ... ',end='')
          repair_options = query.get_repair_options_make_node_input(empty_net)
          print('done.')
        if args.repair==4:
          print('repair mode: define network nodes as input in an experiment ... ',end='')
          repair_options = query.get_repair_options_make_obs_input(empty_net)
          print('done.')
        if args.repair==5:
          print('repair mode: add influence ... ',end='')
          repair_options = query.get_repair_options_add_edges(empty_net)
          print('done.')


        print('\nCompute minimal numbers of necessary repair operations ... ',end='')
        optimum = query.get_minimum_of_repairs(empty_net,repair_options)
        print('done.')

        print('   The data set can be repaired with minimal', optimum[0],'operations.')

        do_repair= raw_input('\nDo you want to compute all possible repair sets? Y/n:')
        if do_repair=="Y":

          print('\nComputing all repair sets with size', optimum[0],'... ',end='')
          models = query.get_minimal_repair_sets(empty_net,repair_options,optimum[0])
          print('done.')

          count    = 1
          oldmodel = 0
          for model in models:
            if oldmodel != model:
              oldmodel = model
              repairs  = model.to_list()
              print('  repair',count,':')
              for r in repairs : print(str(r.arg(0)),end='')
              print(' ')
              count+=1

        print('\nComputing predictions that hold under all repair sets size', optimum[0],'... ',end='')
        model = query.get_predictions_under_minimal_repair(optimum[0],empty_net,repair_options)
        print('done.')
        predictions = model.to_list()
        print(str(len(predictions)),'predictions found:')
        utils.print_predictions(predictions)

    if consistent: #if network is consistent add data
      net_with_data = TermSet(net.union(mu).union(inputs))
      print('\nTesting network with data for consistency ... ',end='')
      consistent = query.is_consistent(net_with_data)
      print("done.")
      if consistent:
        print('   The network and data are consistent.')

        print('\nComputing predictions under consistency ... ',end='')
        model = query.get_predictions_under_consistency(net_with_data)
        print('done.')
        predictions = model.to_list()
        #predictions.sort()
        print(str(len(predictions)), 'predictions found:')
        utils.print_predictions(predictions)


      else:
        print('   The network and the data are inconsistent.')

        if args.mics:
          print('\nComputing minimal inconsistent cores (mic\'s) ... ',end='')
          mics = query.get_minimal_inconsistent_cores(net_with_data)
          print('done.')
          count  = 1
          oldmic = 0
          for mic in mics:
            if oldmic != mic:
              print('mic ',str(count),':',sep='')
              utils.print_mic(mic.to_list(),net.to_list(),mu.to_list())
              count += 1
              oldmic = mic


        repair_options= TermSet()
        print('\nCompute repair options, ',end='')
        if args.repair==1:
          print('repair mode: flip observed variations ... ',end='')
          repair_options = query.get_repair_options_flip_obs(net_with_data)
          print('done.')
        if args.repair==2:
          print('repair mode: flip influences ... ',end='')
          repair_options = query.get_repair_options_flip_edge(net_with_data)
          print('done.')
        if args.repair==3:
          print('repair mode: define network nodes as inputs ... ',end='')
          repair_options = query.get_repair_options_make_node_input(net_with_data)
          print('done.')
        if args.repair==4:
          print('repair mode: define network nodes as input in an experiment ... ',end='')
          repair_options = query.get_repair_options_make_obs_input(net_with_data)
          print('done.')
        if args.repair==5:
          print('repair mode: add influence ... ',end='')
          repair_options = query.get_repair_options_add_edges(net_with_data)
          print('done.')

        print('\nCompute minimal numbers of necessary repair operations ... ',end='')
        optimum = query.get_minimum_of_repairs(net_with_data, repair_options)
        print('done.')

        print('   The data set can be repaired with minimal', optimum.score[0],'operations.')

        if args.list_repairs:

          print('\nComputing all repair sets with size ', optimum.score[0],' ... ',end='')
          models = query.get_minimal_repair_sets(net_with_data, repair_options, optimum.score[0])
          print("done.")
          count    = 1
          oldmodel = 0
          for model in models:
            if oldmodel != model:
              oldmodel = model
              repairs  = model.to_list()
              print("  repair",count,':')
              for r in repairs : print(str(r.arg(0)),end='')
              print(' ')
              count+=1

        #print('Computing subset minimal repairs ...'
        #print('\n', query.subset_minimal_repair_flip_obs(net_with_data)

        print('\nComputing predictions that hold under all repair sets size ', optimum.score[0],' ... ',end='')
        model = query.get_predictions_under_minimal_repair(net_with_data, repair_options, optimum.score[0])
        print("done.")
        predictions = model.to_list()
        #predictions.sort()
        print(str(len(predictions)), 'predictions found:')
        utils.print_predictions(predictions)

    utils.clean_up()