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Diffstat (limited to 'training/mira/mira.py')
| -rwxr-xr-x | training/mira/mira.py | 530 |
1 files changed, 530 insertions, 0 deletions
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() |