From 7facc1f34bcf2fdd328d0ce39d21c5cf7f1896da Mon Sep 17 00:00:00 2001
From: Eva Schlinger <eschling@andrew.cmu.edu>
Date: Fri, 7 Jun 2013 13:20:15 -0400
Subject: python script to run mira

---
 training/mira/mira.py | 530 ++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 530 insertions(+)
 create mode 100755 training/mira/mira.py

(limited to 'training/mira/mira.py')

diff --git a/training/mira/mira.py b/training/mira/mira.py
new file mode 100755
index 00000000..0ad65da0
--- /dev/null
+++ b/training/mira/mira.py
@@ -0,0 +1,530 @@
+#!/usr/bin/env python
+import sys, os, re, shutil
+import subprocess, shlex, glob
+import argparse
+import logging
+import random, time
+import cdec.score
+import gzip, itertools
+
+#mira run script
+#requires pycdec to be built, since it is used for scoring hypothesis
+#translations.
+#matplotlib must be installed for graphing to work
+#email option requires mail
+
+#scoring function using pycdec scoring
+def fast_score(hyps, refs, metric):
+  scorer = cdec.score.Scorer(metric)
+  logging.info('loaded {0} references for scoring with {1}\n'.format(
+                len(refs), metric))
+  if metric=='BLEU':
+    logging.warning('BLEU is ambiguous, assuming IBM_BLEU\n')
+    metric = 'IBM_BLEU'
+  elif metric=='COMBI':
+    logging.warning('COMBI metric is no longer supported, switching to '
+                    'COMB:TER=-0.5;BLEU=0.5\n')
+    metric = 'COMB:TER=-0.5;BLEU=0.5'
+  stats = sum(scorer(r).evaluate(h) for h,r in itertools.izip(hyps,refs))
+  logging.info(stats.detail+'\n')
+  return stats.score
+
+#create new parallel input file in output directory in sgml format
+def enseg(devfile, newfile, gprefix):
+  try:
+    dev = open(devfile)
+    new = open(newfile, 'w')
+  except IOError, msg:
+    logging.error('Error opening source file')
+    raise
+
+  i = 0
+  for line in dev:
+    (src, refs) = line.split(' ||| ', 1)
+    if re.match('\s*<seg', src):
+      if re.search('id="[0-9]+"', src):
+        new.write(line)
+      else:
+        logging.error('When using segments with pre-generated <seg> tags, '
+                      'yout must include a zero based id attribute')
+        sys.exit()
+    else:
+      sgml = '<seg id="{0}"'.format(i)
+      if gprefix:
+        #TODO check if grammar files gzipped or not
+        if os.path.exists('{}.{}.gz'.format(gprefix,i)):
+          sgml += ' grammar="{0}.{1}.gz"'.format(gprefix,i)
+        elif os.path.exists('{}.{}'.format(gprefix,i)):
+          sgml += ' grammar="{}.{}"'.format(gprefix,i)
+        else:
+          logging.error('Could not find grammar files with prefix '
+                        '{}\n'.format(gprefix))
+          sys.exit()
+      sgml += '>{0}</seg> ||| {1}'.format(src, refs)
+      new.write(sgml)
+    i+=1
+  new.close()
+  dev.close()
+  return i
+
+def main():
+  #set logging to write all info messages to stderr
+  logging.basicConfig(level=logging.INFO)
+  script_dir = os.path.dirname(os.path.abspath(sys.argv[0]))
+
+  parser= argparse.ArgumentParser(
+            formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+  parser.add_argument('-d', '--devset', required=True,
+                      help='dev set input file in parallel. '
+                      'format: src ||| ref1 ||| ref2')
+  parser.add_argument('-c', '--config', required=True,
+                      help='decoder configuration file')
+  parser.add_argument('-w','--weights',
+                      help='initial weights file')
+  parser.add_argument('-j', '--jobs', type=int, default=1,
+                      help='number of decoder processes to run in parallel')
+  parser.add_argument('-o','--output-dir', metavar='DIR',
+                      help='directory for intermediate and output files. '
+                      'defaults to mira.(devset name).(time)')
+  parser.add_argument('-e', '--email', 
+                      help='email address to send result report')
+  parser.add_argument('-t', '--test', 
+                      help='test set to decode and evaluate')
+  parser.add_argument('--test-config', 
+                      help='config file for testing. the config file used '
+                      'for tuning feature weights will be used by default.')
+  parser.add_argument('-m', '--metric', default='ibm_bleu',
+                      help='metric to optimize. Example values: '
+                           'ibm_bleu, nist_bleu, Koehn_bleu, TER, Combi')
+  parser.add_argument('--max-iterations', type=int, default=10, metavar='N',
+                      help='maximum number of iterations to run')
+  parser.add_argument('--optimizer', type=int, default=2, choices=range(1,6),
+                      help='learning method to use for weight update.'
+                      ' Choices: 1) SGD, 2) PA MIRA with Selection from Cutting'
+                      ' Plane, 3) Cutting Plane MIRA, 4) PA MIRA,'
+                      ' 5) nbest MIRA with hope, fear, and model constraints')
+  parser.add_argument('--metric-scale', type=int, default=1, metavar='N',
+                      help='scale MT loss by this amount when computing'
+                      ' hope/fear candidates')
+  parser.add_argument('-k', '--kbest-size', type=int, default=250, metavar='N', 
+                      help='size of k-best list to extract from forest')
+  parser.add_argument('--update-size', type=int, metavar='N', 
+                      help='size of k-best list to use for update. defaults to '
+                      'equal kbest-size (applies to optimizer 5)')
+  parser.add_argument('--step-size', type=float, default=0.01, 
+                      help='controls aggresiveness of update')
+  parser.add_argument('--hope', type=int, default=1, choices=range(1,3),
+                     help='how to select hope candidate. options: '
+                     '1) model score - cost, 2) min cost')
+  parser.add_argument('--fear', type=int, default=1, choices=range(1,4),
+                      help='how to select fear candidate. options: '
+                      '1) model score + cost, 2) max cost, 3) max score')
+  parser.add_argument('--sent-approx', action='store_true', 
+                      help='use smoothed sentence-level MT metric')
+  parser.add_argument('--no-pseudo', action='store_true',
+                      help="don't use pseudo document to approximate MT metric")
+  parser.add_argument('--no-unique', action='store_true',
+                      help="don't extract unique k-best from forest")
+  parser.add_argument('-g', '--grammar-prefix', metavar='PATH',
+                      help='path to sentence specific grammar files')
+  parser.add_argument('--pass-suffix', 
+                      help='multipass decoding iteration. see documentation '
+                           'at www.cdec-decoder.org for more information')
+  args = parser.parse_args()
+
+  args.metric = args.metric.upper()
+
+  if not args.update_size:
+    args.update_size = args.kbest_size
+  
+  #TODO fix path to match decode+evaluate (python month 1-12 instead of 0-11)
+  #if an output directory isn't specified, create a unique directory name 
+  #of the form mira.(devset).YYYYMMDD-HHMMSS
+  if not args.output_dir:
+    t = time.localtime()
+    args.output_dir = 'mira.{0}.{1}{2:02}{3:02}-{4:02}{5:02}{6:02}'.format(
+                      os.path.splitext(args.devset)[0], t[0], t[1], t[2],
+                      t[3], t[4], t[5])
+    
+  if not os.path.isabs(args.output_dir):
+    args.output_dir = os.path.abspath(args.output_dir)
+  if os.path.exists(args.output_dir):
+    if len(os.listdir(args.output_dir))>2:
+      logging.error('Error: working directory {0} already exists\n'.format(
+                    args.output_dir))
+      sys.exit()
+  else:
+    os.mkdir(args.output_dir)
+
+  if args.grammar_prefix:
+    if not os.path.isabs(args.grammar_prefix):
+      args.grammar_prefix = os.path.abspath(args.grammar_prefix)
+  
+  script = open(args.output_dir+'/rerun_mira.sh','w')
+  script.write('cd {0}\n'.format(os.getcwd()))
+  script.write(' '.join(sys.argv)+'\n')
+  script.close()
+
+  #create weights.0 file from initial weights file
+  if args.weights:
+    shutil.copy(args.weights,os.path.join(args.output_dir,'weights.0'))
+  else: #if no weights given, use Glue 0 as default
+    weights = open(args.output_dir+'/weights.0','w')
+    weights.write('Glue 0\n')
+    weights.close()
+    args.weights = args.output_dir+'/weights.0'
+  
+  #create mira ini file
+  shutil.copy(args.config,'{0}/kbest_cut_mira.ini'.format(args.output_dir))
+  
+  newdev = args.output_dir+'/dev.input'
+  dev_size = enseg(args.devset, newdev, args.grammar_prefix)
+  args.devset = newdev
+  
+  write_config(args)
+  args.weights, hope_best_fear = optimize(args, script_dir, dev_size)
+  
+  graph_file = graph(args.output_dir, hope_best_fear, args.metric)
+
+  dev_results, dev_bleu = evaluate(args.devset, args.weights, args.config, 
+                         script_dir, args.output_dir)
+  if args.test:
+    if args.test_config:
+      test_results, test_bleu = evaluate(args.test, args.weights, 
+                              args.test_config, script_dir, args.output_dir)
+    else:
+      test_results, test_bleu = evaluate(args.test, args.weights, args.config,
+                              script_dir, args.output_dir)
+  else: 
+    test_results = ''
+    test_bleu = ''
+  logging.info(dev_results+'\n')
+  logging.info(test_results)
+
+  write_report(graph_file, dev_results, dev_bleu, test_results, test_bleu, args)
+
+  logging.info('A graph of the best/hope/fear scores over the iterations '
+               'has been saved to {}\n'.format(graph_file))
+
+  print 'final weights:\n{}\n'.format(args.weights)
+
+#graph of hope/best/fear metric values across all iterations
+def graph(output_dir, hope_best_fear, metric):
+  try: 
+    import matplotlib.pyplot as plt
+  except ImportError:
+    logging.error('Error importing matplotlib. Graphing disabled.\n')
+    return ''
+  max_y = float(max(hope_best_fear['best']))*1.5
+  plt.plot(hope_best_fear['best'], label='best')
+  plt.plot(hope_best_fear['hope'], label='hope')
+  plt.plot(hope_best_fear['fear'], label='fear')
+  plt.axis([0,len(hope_best_fear['fear'])-1,0,max_y])
+  plt.xlabel('Iteration')
+  plt.ylabel(metric)
+  plt.legend()
+  graph_file = output_dir+'/mira.pdf'
+  plt.savefig(graph_file)
+  return graph_file
+
+#evaluate a given test set using decode-and-evaluate.pl
+def evaluate(testset, weights, ini, script_dir, out_dir):
+  evaluator = '{}/../utils/decode-and-evaluate.pl'.format(script_dir)
+  try:
+    p = subprocess.Popen([evaluator, '-c', ini, '-w', weights, '-i', testset, 
+                         '-d', out_dir], stdout=subprocess.PIPE)
+    results, err = p.communicate()
+    bleu, results = results.split('\n',1)
+  except subprocess.CalledProcessError:
+    logging.error('Evalutation of {} failed'.format(testset))
+    results = ''
+    bleu = ''
+  return results, bleu
+
+#print a report to out_dir/mira.results
+#send email with results if email was given
+def write_report(graph_file, dev_results, dev_bleu, 
+                 test_results, test_bleu, args):
+  features, top, bottom = weight_stats(args.weights) 
+  top = [f+' '+str(w) for f,w in top]
+  bottom = [f+' '+str(w) for f,w in bottom]
+  subject = 'MIRA {0} {1:7}'.format(os.path.basename(args.devset), dev_bleu)
+  if args.test:
+    subject += ' {0} {1:7}'.format(os.path.basename(args.test), test_bleu)
+
+  message = ('MIRA has finished running. '+
+            'The final weights can be found at \n{}\n'.format(args.weights)+
+            'Average weights across all iterations '+
+            '\n{}/weights.average\n'.format(args.output_dir)+
+            'Weights were calculated for {} features\n\n'.format(features)+
+            '5 highest weights:\n{}\n\n'.format('\n'.join(top))+
+            '5 lowest weights:\n{}\n'.format('\n'.join(bottom)))
+  
+  if dev_results:
+    message += '\nEvaluation: dev set\n{}'.format(dev_results)
+  if test_results:
+    message += '\nEvaluation: test set\n{}'.format(test_results)
+ 
+  out = open(args.output_dir+'/mira.results','w')
+  out.write(message)
+  out.close()
+ 
+  if args.email:
+    email_process = subprocess.Popen(['mail', '-s', subject, '-a', 
+                        graph_file, args.email], stdin = subprocess.PIPE)
+    email_process.communicate(message)
+
+#feature weights stats for report
+def weight_stats(weight_file):
+  f = open(weight_file)
+  features = []
+  for line in f:
+    feat, weight = line.strip().split()
+    features.append((feat,float(weight)))
+  features.sort(key=lambda a: a[1], reverse=True)
+  return len(features), features[:5], features[-5:]
+
+#create source and refs files from parallel devset
+#TODO remove when kbest_cut_mira changed to take parallel input
+def split_devset(dev, outdir):
+  parallel = open(dev)
+  source = open(outdir+'/source.input','w')
+  refs = open(outdir+'/refs.input', 'w')
+  references = []
+  for line in parallel:
+    s,r = line.strip().split(' ||| ',1)
+    source.write(s+'\n')
+    refs.write(r+'\n')
+    references.append(r)
+  source.close()
+  refs.close()
+  return (outdir+'/source.input', outdir+'/refs.input')
+
+def optimize(args, script_dir, dev_size):
+  parallelize = script_dir+'/../utils/parallelize.pl'
+  decoder = script_dir+'/kbest_cut_mira'
+  (source, refs) = split_devset(args.devset, args.output_dir)
+  port = random.randint(15000,50000)
+  num_features = 0
+  last_p_score = 0
+  best_score_iter = -1
+  best_score = -1
+  i = 0
+  hope_best_fear = {'hope':[],'best':[],'fear':[]}
+  #main optimization loop
+  while i<args.max_iterations:
+    logging.info('\n\nITERATION {}\n========\n'.format(i))
+    logging.info('using port {}\n'.format(port))
+
+    #iteration specific files
+    runfile = args.output_dir+'/run.raw.'+str(i)
+    onebestfile = args.output_dir+'/1best.'+str(i)
+    logdir = args.output_dir+'/logs.'+str(i)
+    decoderlog = logdir+'/decoder.sentserver.log.'+str(i)
+    weightdir = args.output_dir+'/weights.pass'+str(i)
+    os.mkdir(logdir)
+    os.mkdir(weightdir)
+    
+    logging.info('RUNNING DECODER AT {}'.format(time.asctime()))
+    weightsfile = args.output_dir+'/weights.'+str(i)
+    logging.info('ITER {}\n'.format(i))
+    curr_pass = '0{}'.format(i)
+    decoder_cmd = ('{0} -c {1} -w {2} -r{3} -m {4} -s {5} -b {6} -k {7} -o {8}'
+                   ' -p {9} -O {10} -D {11} -h {12} -f {13} -C {14}').format(
+                   decoder, args.config, weightsfile, refs, args.metric,
+                   args.metric_scale, args.update_size, args.kbest_size, 
+                   args.optimizer, curr_pass, weightdir, args.output_dir,
+                   args.hope, args.fear, args.step_size)
+    if not args.no_unique: 
+      decoder_cmd += ' -u'
+    if args.sent_approx:
+      decoder_cmd += ' -a'
+    if not args.no_pseudo:
+      decoder_cmd += ' -e'
+    
+    #always use fork 
+    parallel_cmd = '{0} --use-fork -e {1} -j {2} --'.format(
+                    parallelize, logdir, args.jobs)
+    
+    cmd = parallel_cmd + ' ' + decoder_cmd
+    logging.info('COMMAND: \n{}\n'.format(cmd))
+   
+    dlog = open(decoderlog,'w')
+    runf = open(runfile,'w')
+    retries = 0
+    num_topbest = 0
+
+    while retries < 6:
+      #call decoder through parallelize.pl
+      p1 = subprocess.Popen(['cat', source], stdout=subprocess.PIPE)
+      exit_code = subprocess.call(shlex.split(cmd), stderr=dlog, stdout=runf, 
+                                  stdin=p1.stdout)
+      p1.stdout.close()
+      
+      if exit_code:
+        logging.error('Failed with exit code {}\n'.format(exit_code))
+        sys.exit(exit_code)
+
+      try:
+        f = open(runfile)
+      except IOError, msg:
+        logging.error('Unable to open {}\n'.format(runfile))
+        sys.exit()
+      
+      num_topbest = sum(1 for line in f)
+      f.close()
+      if num_topbest == dev_size: break
+      logging.warning('Incorrect number of top best. '
+                      'Waiting for distributed filesystem and retrying.')
+      time.sleep(10)
+      retries += 1
+    
+    if dev_size != num_topbest:
+      logging.error("Dev set contains "+dev_size+" sentences, but we don't "
+                    "have topbest for all of these. Decoder failure? "
+                    " Check "+decoderlog+'\n')
+      sys.exit()
+    dlog.close()
+    runf.close()
+
+    #write best, hope, and fear translations
+    run = open(runfile)
+    H = open(runfile+'.H', 'w')
+    B = open(runfile+'.B', 'w')
+    F = open(runfile+'.F', 'w')
+    hopes = []
+    bests = []
+    fears = []
+    for line in run:
+      hope, best, fear = line.split(' ||| ')
+      hopes.append(hope)
+      bests.append(best)
+      fears.append(fear)
+      H.write('{}\n'.format(hope))
+      B.write('{}\n'.format(best))
+      F.write('{}\n'.format(fear))
+    run.close()
+    H.close()
+    B.close()
+    F.close()
+
+    #gzip runfiles and log files to save space
+    gzip_file(runfile)
+    gzip_file(decoderlog)
+
+    ref_file = open(refs)
+    references = [line.split(' ||| ') for line in 
+                  ref_file.read().strip().split('\n')]
+    ref_file.close()
+    #get score for best hypothesis translations, hope and fear translations
+    dec_score = fast_score(bests, references, args.metric)
+    dec_score_h = fast_score(hopes, references, args.metric)
+    dec_score_f = fast_score(fears, references, args.metric)
+    
+    hope_best_fear['hope'].append(dec_score)
+    hope_best_fear['best'].append(dec_score_h)
+    hope_best_fear['fear'].append(dec_score_f)
+    logging.info('DECODER SCORE: {0} HOPE: {1} FEAR: {2}\n'.format(
+                  dec_score, dec_score_h, dec_score_f))
+    if dec_score > best_score:
+      best_score_iter = i
+      best_score = dec_score
+
+    new_weights_file = '{}/weights.{}'.format(args.output_dir, i+1)
+    last_weights_file = '{}/weights.{}'.format(args.output_dir, i)
+    i += 1
+    weight_files = weightdir+'/weights.mira-pass*.*[0-9].gz'
+    average_weights(new_weights_file, weight_files)
+
+  logging.info('\nBEST ITER: {} :: {}\n\n'.format(
+               best_score_iter, best_score))
+  weights_final = args.output_dir+'/weights.final'
+  shutil.copy(last_weights_file, weights_final)
+  average_final_weights(args.output_dir)
+  
+  return weights_final, hope_best_fear
+
+#TODO
+#create a weights file with the average of the weights from each iteration
+def average_final_weights(out_dir):
+  logging.info('Average of weights from each iteration\n')
+  weight_files = glob.glob(out_dir+'/weights.[1-9]*')
+  features = {}
+  for path in weight_files:
+    weights = open(path)
+    for line in weights:
+      f, w = line.strip().split(' ', 1)
+      if f in features:
+        features[f] += float(w)
+      else:
+        features[f] = float(w)
+    weights.close()
+
+  out = open(out_dir+'/weights.average','w')
+  for f in iter(features):
+    out.write('{} {}\n'.format(f,features[f]/len(weight_files)))
+  logging.info('An average weights file can be found at' 
+               '\n{}\n'.format(out_dir+'/weights.average'))
+
+#create gzipped version of given file with name filename.gz
+# and delete original file
+def gzip_file(filename):
+  gzip_file = gzip.open(filename+'.gz','wb')
+  f = open(filename)
+  gzip_file.writelines(f)
+  f.close()
+  gzip_file.close()
+  os.remove(filename)
+
+#average the weights for a given pass
+def average_weights(new_weights, weight_files):
+  logging.info('AVERAGE {} {}\n'.format(new_weights, weight_files))
+  feature_weights = {}
+  total_mult = 0.0
+  for path in glob.glob(weight_files):
+    score = gzip.open(path)
+    mult = 0
+    logging.info('FILE {}\n'.format(path))
+    msg, ran, mult = score.readline().strip().split(' ||| ')
+    logging.info('Processing {} {}'.format(ran, mult))
+    for line in score:
+      f,w = line.split(' ',1)
+      if f in feature_weights:
+        feature_weights[f]+= float(mult)*float(w)
+      else: 
+        feature_weights[f] = float(mult)*float(w)
+    total_mult += float(mult)
+    score.close()
+  
+  #write new weights to outfile
+  out = open(new_weights, 'w')
+  for f in iter(feature_weights):
+    avg = feature_weights[f]/total_mult
+    logging.info('{} {} {} ||| Printing {} {}\n'.format(f,feature_weights[f], 
+                 total_mult, f, avg))
+    out.write('{} {}\n'.format(f,avg))
+
+def write_config(args):
+  config = ('\n'
+            'DECODER: '
+            '/usr0/home/eschling/cdec/training/mira/kbest_cut_mira\n'
+            'INI FILE: '+args.config+'\n'
+            'WORKING DIRECTORY: '+args.output_dir+'\n'
+            'DEVSET: '+args.devset+'\n'
+            'EVAL METRIC: '+args.metric+'\n'
+            'MAX ITERATIONS: '+str(args.max_iterations)+'\n'
+            'DECODE NODES: '+str(args.jobs)+'\n'
+            'INITIAL WEIGHTS: '+args.weights+'\n')
+  if args.grammar_prefix:
+    config += 'GRAMMAR PREFIX: '+str(args.grammar_prefix)+'\n'
+  if args.test:
+    config += 'TEST SET: '+args.test+'\n'
+  if args.test_config:
+    config += 'TEST CONFIG: '+args.test_config+'\n'
+  if args.email:
+    config += 'EMAIL: '+args.email+'\n'
+           
+  logging.info(config)
+
+if __name__=='__main__':
+  main()
-- 
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