finalized merge

6 files changed, 1323 insertions, 0 deletions

diff --git a/pro-train/Makefile.am b/pro-train/Makefile.am
new file mode 100644
index 00000000..fdaf43e2
--- /dev/null
+++ b/pro-train/Makefile.am
@@ -0,0 +1,13 @@
+bin_PROGRAMS = \
+  mr_pro_map \
+  mr_pro_reduce
+
+TESTS = lo_test
+
+mr_pro_map_SOURCES = mr_pro_map.cc
+mr_pro_map_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a -lz
+
+mr_pro_reduce_SOURCES = mr_pro_reduce.cc
+mr_pro_reduce_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/training/optimize.o $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a -lz
+
+AM_CPPFLAGS = -W -Wall -Wno-sign-compare $(GTEST_CPPFLAGS) -I$(top_srcdir)/utils -I$(top_srcdir)/decoder -I$(top_srcdir)/mteval -I$(top_srcdir)/training
diff --git a/pro-train/README.shared-mem b/pro-train/README.shared-mem
new file mode 100644
index 00000000..7728efc0
--- /dev/null
+++ b/pro-train/README.shared-mem
@@ -0,0 +1,9 @@
+If you want to run dist-vest.pl on a very large shared memory machine, do the
+following:
+
+  ./dist-vest.pl --use-make I --decode-nodes J --weights weights.init --source-file=dev.src --ref-files=dev.ref.* cdec.ini
+
+This will use I jobs for doing the line search and J jobs to run the decoder. Typically, since the
+decoder must load grammars, language models, etc., J should be smaller than I, but this will depend
+on the system you are running on and the complexity of the models used for decoding.
+
diff --git a/pro-train/dist-pro.pl b/pro-train/dist-pro.pl
new file mode 100755
index 00000000..dbfa329a
--- /dev/null
+++ b/pro-train/dist-pro.pl
@@ -0,0 +1,657 @@
+#!/usr/bin/env perl
+use strict;
+my @ORIG_ARGV=@ARGV;
+use Cwd qw(getcwd);
+my $SCRIPT_DIR; BEGIN { use Cwd qw/ abs_path /; use File::Basename; $SCRIPT_DIR = dirname(abs_path($0)); push @INC, $SCRIPT_DIR, "$SCRIPT_DIR/../environment"; }
+
+# Skip local config (used for distributing jobs) if we're running in local-only mode
+use LocalConfig;
+use Getopt::Long;
+use IPC::Open2;
+use POSIX ":sys_wait_h";
+my $QSUB_CMD = qsub_args(mert_memory());
+
+my $VEST_DIR="$SCRIPT_DIR/../vest";
+require "$VEST_DIR/libcall.pl";
+
+# Default settings
+my $srcFile;
+my $refFiles;
+my $bin_dir = $SCRIPT_DIR;
+die "Bin directory $bin_dir missing/inaccessible" unless -d $bin_dir;
+my $FAST_SCORE="$bin_dir/../mteval/fast_score";
+die "Can't execute $FAST_SCORE" unless -x $FAST_SCORE;
+my $MAPINPUT = "$bin_dir/mr_pro_generate_mapper_input.pl";
+my $MAPPER = "$bin_dir/mr_pro_map";
+my $REDUCER = "$bin_dir/mr_pro_reduce";
+my $parallelize = "$VEST_DIR/parallelize.pl";
+my $libcall = "$VEST_DIR/libcall.pl";
+my $sentserver = "$VEST_DIR/sentserver";
+my $sentclient = "$VEST_DIR/sentclient";
+my $LocalConfig = "$SCRIPT_DIR/../environment/LocalConfig.pm";
+
+my $SCORER = $FAST_SCORE;
+die "Can't find $MAPPER" unless -x $MAPPER;
+my $cdec = "$bin_dir/../decoder/cdec";
+die "Can't find decoder in $cdec" unless -x $cdec;
+die "Can't find $parallelize" unless -x $parallelize;
+die "Can't find $libcall" unless -e $libcall;
+my $decoder = $cdec;
+my $lines_per_mapper = 30;
+my $iteration = 1;
+my $run_local = 0;
+my $best_weights;
+my $max_iterations = 30;
+my $decode_nodes = 15;   # number of decode nodes
+my $pmem = "4g";
+my $disable_clean = 0;
+my %seen_weights;
+my $help = 0;
+my $epsilon = 0.0001;
+my $dryrun = 0;
+my $last_score = -10000000;
+my $metric = "ibm_bleu";
+my $dir;
+my $iniFile;
+my $weights;
+my $use_make;  # use make to parallelize
+my $usefork;
+my $initial_weights;
+my $pass_suffix = '';
+my $cpbin=1;
+
+# regularization strength
+my $tune_regularizer = 0;
+my $reg = 1e-2;
+
+# Process command-line options
+Getopt::Long::Configure("no_auto_abbrev");
+if (GetOptions(
+	"decode-nodes=i" => \$decode_nodes,
+	"dont-clean" => \$disable_clean,
+	"pass-suffix=s" => \$pass_suffix,
+        "use-fork" => \$usefork,
+	"dry-run" => \$dryrun,
+	"epsilon=s" => \$epsilon,
+	"help" => \$help,
+        "weights=s" => \$initial_weights,
+	"tune-regularizer" => \$tune_regularizer,
+	"reg=f" => \$reg,
+	"local" => \$run_local,
+	"use-make=i" => \$use_make,
+	"max-iterations=i" => \$max_iterations,
+	"pmem=s" => \$pmem,
+        "cpbin!" => \$cpbin,
+	"ref-files=s" => \$refFiles,
+	"metric=s" => \$metric,
+	"source-file=s" => \$srcFile,
+	"workdir=s" => \$dir,
+) == 0 || @ARGV!=1 || $help) {
+	print_help();
+	exit;
+}
+
+if ($usefork) { $usefork = "--use-fork"; } else { $usefork = ''; }
+
+if ($metric =~ /^(combi|ter)$/i) {
+  $lines_per_mapper = 5;
+}
+
+($iniFile) = @ARGV;
+
+
+sub write_config;
+sub enseg;
+sub print_help;
+
+my $nodelist;
+my $host =check_output("hostname"); chomp $host;
+my $bleu;
+my $interval_count = 0;
+my $logfile;
+my $projected_score;
+
+# used in sorting scores
+my $DIR_FLAG = '-r';
+if ($metric =~ /^ter$|^aer$/i) {
+  $DIR_FLAG = '';
+}
+
+my $refs_comma_sep = get_comma_sep_refs('r',$refFiles);
+
+unless ($dir){
+	$dir = "protrain";
+}
+unless ($dir =~ /^\//){  # convert relative path to absolute path
+	my $basedir = check_output("pwd");
+	chomp $basedir;
+	$dir = "$basedir/$dir";
+}
+
+
+# Initializations and helper functions
+srand;
+
+my @childpids = ();
+my @cleanupcmds = ();
+
+sub cleanup {
+	print STDERR "Cleanup...\n";
+	for my $pid (@childpids){ unchecked_call("kill $pid"); }
+	for my $cmd (@cleanupcmds){ unchecked_call("$cmd"); }
+	exit 1;
+};
+# Always call cleanup, no matter how we exit
+*CORE::GLOBAL::exit = 
+    sub{ cleanup(); }; 
+$SIG{INT} = "cleanup";
+$SIG{TERM} = "cleanup";
+$SIG{HUP} = "cleanup";
+
+my $decoderBase = check_output("basename $decoder"); chomp $decoderBase;
+my $newIniFile = "$dir/$decoderBase.ini";
+my $inputFileName = "$dir/input";
+my $user = $ENV{"USER"};
+
+
+# process ini file
+-e $iniFile || die "Error: could not open $iniFile for reading\n";
+open(INI, $iniFile);
+
+use File::Basename qw(basename);
+#pass bindir, refs to vars holding bin
+sub modbin {
+    local $_;
+    my $bindir=shift;
+    check_call("mkdir -p $bindir");
+    -d $bindir || die "couldn't make bindir $bindir";
+    for (@_) {
+        my $src=$$_;
+        $$_="$bindir/".basename($src);
+        check_call("cp -p $src $$_");
+    }
+}
+sub dirsize {
+    opendir ISEMPTY,$_[0];
+    return scalar(readdir(ISEMPTY))-1;
+}
+my @allweights;
+if ($dryrun){
+	write_config(*STDERR);
+	exit 0;
+} else {
+	if (-e $dir && dirsize($dir)>1 && -e "$dir/hgs" ){ # allow preexisting logfile, binaries, but not dist-pro.pl outputs
+	  die "ERROR: working dir $dir already exists\n\n";
+	} else {
+		-e $dir || mkdir $dir;
+		mkdir "$dir/hgs";
+        modbin("$dir/bin",\$LocalConfig,\$cdec,\$SCORER,\$MAPINPUT,\$MAPPER,\$REDUCER,\$parallelize,\$sentserver,\$sentclient,\$libcall) if $cpbin;
+    mkdir "$dir/scripts";
+        my $cmdfile="$dir/rerun-pro.sh";
+        open CMD,'>',$cmdfile;
+        print CMD "cd ",&getcwd,"\n";
+#        print CMD &escaped_cmdline,"\n"; #buggy - last arg is quoted.
+        my $cline=&cmdline."\n";
+        print CMD $cline;
+        close CMD;
+        print STDERR $cline;
+        chmod(0755,$cmdfile);
+	check_call("cp $initial_weights $dir/weights.0");
+	die "Can't find weights.0" unless (-e "$dir/weights.0");
+	}
+	write_config(*STDERR);
+}
+
+
+# Generate initial files and values
+check_call("cp $iniFile $newIniFile");
+$iniFile = $newIniFile;
+
+my $newsrc = "$dir/dev.input";
+enseg($srcFile, $newsrc);
+$srcFile = $newsrc;
+my $devSize = 0;
+open F, "<$srcFile" or die "Can't read $srcFile: $!";
+while(<F>) { $devSize++; }
+close F;
+
+unless($best_weights){ $best_weights = $weights; }
+unless($projected_score){ $projected_score = 0.0; }
+$seen_weights{$weights} = 1;
+
+my $random_seed = int(time / 1000);
+my $lastWeightsFile;
+my $lastPScore = 0;
+# main optimization loop
+while (1){
+	print STDERR "\n\nITERATION $iteration\n==========\n";
+
+	if ($iteration > $max_iterations){
+		print STDERR "\nREACHED STOPPING CRITERION: Maximum iterations\n";
+		last;
+	}
+	# iteration-specific files
+	my $runFile="$dir/run.raw.$iteration";
+	my $onebestFile="$dir/1best.$iteration";
+	my $logdir="$dir/logs.$iteration";
+	my $decoderLog="$logdir/decoder.sentserver.log.$iteration";
+	my $scorerLog="$logdir/scorer.log.$iteration";
+	check_call("mkdir -p $logdir");
+
+
+	#decode
+	print STDERR "RUNNING DECODER AT ";
+	print STDERR unchecked_output("date");
+	my $im1 = $iteration - 1;
+	my $weightsFile="$dir/weights.$im1";
+        push @allweights, "-w $dir/weights.$im1";
+        `rm -f $dir/hgs/*.gz`;
+	my $decoder_cmd = "$decoder -c $iniFile --weights$pass_suffix $weightsFile -O $dir/hgs";
+	my $pcmd;
+	if ($run_local) {
+		$pcmd = "cat $srcFile |";
+	} elsif ($use_make) {
+	    # TODO: Throw error when decode_nodes is specified along with use_make
+		$pcmd = "cat $srcFile | $parallelize --use-fork -p $pmem -e $logdir -j $use_make --";
+	} else {
+		$pcmd = "cat $srcFile | $parallelize $usefork -p $pmem -e $logdir -j $decode_nodes --";
+	}
+	my $cmd = "$pcmd $decoder_cmd 2> $decoderLog 1> $runFile";
+	print STDERR "COMMAND:\n$cmd\n";
+	check_bash_call($cmd);
+        my $num_hgs;
+        my $num_topbest;
+        my $retries = 0;
+	while($retries < 5) {
+	    $num_hgs = check_output("ls $dir/hgs/*.gz | wc -l");
+	    $num_topbest = check_output("wc -l < $runFile");
+	    print STDERR "NUMBER OF HGs: $num_hgs\n";
+	    print STDERR "NUMBER OF TOP-BEST HYPs: $num_topbest\n";
+	    if($devSize == $num_hgs && $devSize == $num_topbest) {
+		last;
+	    } else {
+		print STDERR "Incorrect number of hypergraphs or topbest. Waiting for distributed filesystem and retrying...\n";
+		sleep(3);
+	    }
+	    $retries++;
+	}
+	die "Dev set contains $devSize sentences, but we don't have topbest and hypergraphs for all these! Decoder failure? Check $decoderLog\n" if ($devSize != $num_hgs || $devSize != $num_topbest);
+	my $dec_score = check_output("cat $runFile | $SCORER $refs_comma_sep -l $metric");
+	chomp $dec_score;
+	print STDERR "DECODER SCORE: $dec_score\n";
+
+	# save space
+	check_call("gzip -f $runFile");
+	check_call("gzip -f $decoderLog");
+
+	# run optimizer
+	print STDERR "RUNNING OPTIMIZER AT ";
+	print STDERR unchecked_output("date");
+	print STDERR " - GENERATE TRAINING EXEMPLARS\n";
+	my $mergeLog="$logdir/prune-merge.log.$iteration";
+
+	my $score = 0;
+	my $icc = 0;
+	my $inweights="$dir/weights.$im1";
+	$cmd="$MAPINPUT $dir/hgs > $dir/agenda.$im1";
+	print STDERR "COMMAND:\n$cmd\n";
+	check_call($cmd);
+	check_call("mkdir -p $dir/splag.$im1");
+	$cmd="split -a 3 -l $lines_per_mapper $dir/agenda.$im1 $dir/splag.$im1/mapinput.";
+	print STDERR "COMMAND:\n$cmd\n";
+	check_call($cmd);
+	opendir(DIR, "$dir/splag.$im1") or die "Can't open directory: $!";
+	my @shards = grep { /^mapinput\./ } readdir(DIR);
+	closedir DIR;
+	die "No shards!" unless scalar @shards > 0;
+	my $joblist = "";
+	my $nmappers = 0;
+	@cleanupcmds = ();
+	my %o2i = ();
+	my $first_shard = 1;
+	my $mkfile; # only used with makefiles
+	my $mkfilename;
+	if ($use_make) {
+		$mkfilename = "$dir/splag.$im1/domap.mk";
+		open $mkfile, ">$mkfilename" or die "Couldn't write $mkfilename: $!";
+		print $mkfile "all: $dir/splag.$im1/map.done\n\n";
+	}
+	my @mkouts = ();  # only used with makefiles
+	my @mapoutputs = ();
+	for my $shard (@shards) {
+		my $mapoutput = $shard;
+		my $client_name = $shard;
+		$client_name =~ s/mapinput.//;
+		$client_name = "pro.$client_name";
+		$mapoutput =~ s/mapinput/mapoutput/;
+		push @mapoutputs, "$dir/splag.$im1/$mapoutput";
+		$o2i{"$dir/splag.$im1/$mapoutput"} = "$dir/splag.$im1/$shard";
+		my $script = "$MAPPER -s $srcFile -l $metric $refs_comma_sep -w $inweights -K $dir/kbest < $dir/splag.$im1/$shard > $dir/splag.$im1/$mapoutput";
+		if ($run_local) {
+			print STDERR "COMMAND:\n$script\n";
+			check_bash_call($script);
+		} elsif ($use_make) {
+			my $script_file = "$dir/scripts/map.$shard";
+			open F, ">$script_file" or die "Can't write $script_file: $!";
+			print F "#!/bin/bash\n";
+			print F "$script\n";
+			close F;
+			my $output = "$dir/splag.$im1/$mapoutput";
+			push @mkouts, $output;
+			chmod(0755, $script_file) or die "Can't chmod $script_file: $!";
+			if ($first_shard) { print STDERR "$script\n"; $first_shard=0; }
+			print $mkfile "$output: $dir/splag.$im1/$shard\n\t$script_file\n\n";
+		} else {
+			my $script_file = "$dir/scripts/map.$shard";
+			open F, ">$script_file" or die "Can't write $script_file: $!";
+			print F "$script\n";
+			close F;
+			if ($first_shard) { print STDERR "$script\n"; $first_shard=0; }
+
+			$nmappers++;
+			my $qcmd = "$QSUB_CMD -N $client_name -o /dev/null -e $logdir/$client_name.ER $script_file";
+			my $jobid = check_output("$qcmd");
+			chomp $jobid;
+			$jobid =~ s/^(\d+)(.*?)$/\1/g;
+			$jobid =~ s/^Your job (\d+) .*$/\1/;
+		 	push(@cleanupcmds, "qdel $jobid 2> /dev/null");
+			print STDERR " $jobid";
+			if ($joblist == "") { $joblist = $jobid; }
+			else {$joblist = $joblist . "\|" . $jobid; }
+		}
+	}
+	my @dev_outs = ();
+	my @devtest_outs = ();
+	if ($tune_regularizer) {
+		for (my $i = 0; $i < scalar @mapoutputs; $i++) {
+			if ($i % 3 == 1) {
+				push @devtest_outs, $mapoutputs[$i];
+			} else {
+				push @dev_outs, $mapoutputs[$i];
+			}
+		}
+		if (scalar @devtest_outs == 0) {
+			die "Not enough training instances for regularization tuning! Rerun without --tune-regularizer\n";
+		}
+	} else {
+		@dev_outs = @mapoutputs;
+	}
+	if ($run_local) {
+		print STDERR "\nCompleted extraction of training exemplars.\n";
+	} elsif ($use_make) {
+		print $mkfile "$dir/splag.$im1/map.done: @mkouts\n\ttouch $dir/splag.$im1/map.done\n\n";
+		close $mkfile;
+		my $mcmd = "make -j $use_make -f $mkfilename";
+		print STDERR "\nExecuting: $mcmd\n";
+		check_call($mcmd);
+	} else {
+		print STDERR "\nLaunched $nmappers mappers.\n";
+      		sleep 8;
+		print STDERR "Waiting for mappers to complete...\n";
+		while ($nmappers > 0) {
+		  sleep 5;
+		  my @livejobs = grep(/$joblist/, split(/\n/, unchecked_output("qstat | grep -v ' C '")));
+		  $nmappers = scalar @livejobs;
+		}
+		print STDERR "All mappers complete.\n";
+	}
+	my $tol = 0;
+	my $til = 0;
+	my $dev_test_file = "$dir/splag.$im1/devtest.gz";
+	if ($tune_regularizer) {
+		my $cmd = "cat @devtest_outs | gzip > $dev_test_file";
+		check_bash_call($cmd);
+		die "Can't find file $dev_test_file" unless -f $dev_test_file;
+	}
+        #print STDERR "MO: @mapoutputs\n";
+	for my $mo (@mapoutputs) {
+		#my $olines = get_lines($mo);
+		#my $ilines = get_lines($o2i{$mo});
+		#die "$mo: no training instances generated!" if $olines == 0;
+	}
+	print STDERR "\nRUNNING CLASSIFIER (REDUCER)\n";
+	print STDERR unchecked_output("date");
+	$cmd="cat @dev_outs | $REDUCER -w $dir/weights.$im1 -s $reg";
+	if ($tune_regularizer) {
+		$cmd .= " -T -t $dev_test_file";
+	}
+        $cmd .= " > $dir/weights.$iteration";
+	print STDERR "COMMAND:\n$cmd\n";
+	check_bash_call($cmd);
+	$lastWeightsFile = "$dir/weights.$iteration";
+	if ($tune_regularizer) {
+		open W, "<$lastWeightsFile" or die "Can't read $lastWeightsFile: $!";
+		my $line = <W>;
+		close W;
+		my ($sharp, $label, $nreg) = split /\s|=/, $line;
+		print STDERR "REGULARIZATION STRENGTH ($label) IS $nreg\n";
+		$reg = $nreg;
+		# only tune regularizer on first iteration?
+		$tune_regularizer = 0;
+	}
+	$lastPScore = $score;
+	$iteration++;
+	print STDERR "\n==========\n";
+}
+
+print STDERR "\nFINAL WEIGHTS: $lastWeightsFile\n(Use -w <this file> with the decoder)\n\n";
+
+print STDOUT "$lastWeightsFile\n";
+
+exit 0;
+
+sub get_lines {
+  my $fn = shift @_;
+  open FL, "<$fn" or die "Couldn't read $fn: $!";
+  my $lc = 0;
+  while(<FL>) { $lc++; }
+  return $lc;
+}
+
+sub get_comma_sep_refs {
+  my ($r,$p) = @_;
+  my $o = check_output("echo $p");
+  chomp $o;
+  my @files = split /\s+/, $o;
+  return "-$r " . join(" -$r ", @files);
+}
+
+sub read_weights_file {
+  my ($file) = @_;
+  open F, "<$file" or die "Couldn't read $file: $!";
+  my @r = ();
+  my $pm = -1;
+  while(<F>) {
+    next if /^#/;
+    next if /^\s*$/;
+    chomp;
+    if (/^(.+)\s+(.+)$/) {
+      my $m = $1;
+      my $w = $2;
+      die "Weights out of order: $m <= $pm" unless $m > $pm;
+      push @r, $w;
+    } else {
+      warn "Unexpected feature name in weight file: $_";
+    }
+  }
+  close F;
+  return join ' ', @r;
+}
+
+# subs
+sub write_config {
+	my $fh = shift;
+	my $cleanup = "yes";
+	if ($disable_clean) {$cleanup = "no";}
+
+	print $fh "\n";
+	print $fh "DECODER:          $decoder\n";
+	print $fh "INI FILE:         $iniFile\n";
+	print $fh "WORKING DIR:      $dir\n";
+	print $fh "SOURCE (DEV):     $srcFile\n";
+	print $fh "REFS (DEV):       $refFiles\n";
+	print $fh "EVAL METRIC:      $metric\n";
+	print $fh "MAX ITERATIONS:   $max_iterations\n";
+	print $fh "DECODE NODES:     $decode_nodes\n";
+	print $fh "HEAD NODE:        $host\n";
+	print $fh "PMEM (DECODING):  $pmem\n";
+	print $fh "CLEANUP:          $cleanup\n";
+}
+
+sub update_weights_file {
+  my ($neww, $rfn, $rpts) = @_;
+  my @feats = @$rfn;
+  my @pts = @$rpts;
+  my $num_feats = scalar @feats;
+  my $num_pts = scalar @pts;
+  die "$num_feats (num_feats) != $num_pts (num_pts)" unless $num_feats == $num_pts;
+  open G, ">$neww" or die;
+  for (my $i = 0; $i < $num_feats; $i++) {
+    my $f = $feats[$i];
+    my $lambda = $pts[$i];
+    print G "$f $lambda\n";
+  }
+  close G;
+}
+
+sub enseg {
+	my $src = shift;
+	my $newsrc = shift;
+	open(SRC, $src);
+	open(NEWSRC, ">$newsrc");
+	my $i=0;
+	while (my $line=<SRC>){
+		chomp $line;
+		if ($line =~ /^\s*<seg/i) {
+		    if($line =~ /id="[0-9]+"/) {
+			print NEWSRC "$line\n";
+		    } else {
+			die "When using segments with pre-generated <seg> tags, you must include a zero-based id attribute";
+		    }
+		} else {
+			print NEWSRC "<seg id=\"$i\">$line</seg>\n";
+		}
+		$i++;
+	}
+	close SRC;
+	close NEWSRC;
+	die "Empty dev set!" if ($i == 0);
+}
+
+sub print_help {
+
+	my $executable = check_output("basename $0"); chomp $executable;
+    print << "Help";
+
+Usage: $executable [options] <ini file>
+
+	$executable [options] <ini file>
+		Runs a complete MERT optimization and test set decoding, using
+		the decoder configuration in ini file.  Note that many of the
+		options have default values that are inferred automatically
+		based on certain conventions.  For details, refer to descriptions
+		of the options --decoder, --weights, and --workdir.
+
+Required:
+
+	--ref-files <files>
+		Dev set ref files.  This option takes only a single string argument.
+		To use multiple files (including file globbing), this argument should
+		be quoted.
+
+	--source-file <file>
+		Dev set source file.
+
+	--weights <file>
+		Initial weights file (use empty file to start from 0)
+
+General options:
+
+	--local
+		Run the decoder and optimizer locally with a single thread.
+
+	--decode-nodes <I>
+		Number of decoder processes to run in parallel. [default=15]
+
+	--help
+		Print this message and exit.
+
+	--max-iterations <M>
+		Maximum number of iterations to run.  If not specified, defaults
+		to 10.
+
+	--metric <method>
+		Metric to optimize.
+		Example values: IBM_BLEU, NIST_BLEU, Koehn_BLEU, TER, Combi
+
+	--pass-suffix <S>
+		If the decoder is doing multi-pass decoding, the pass suffix "2",
+		"3", etc., is used to control what iteration of weights is set.
+
+	--pmem <N>
+		Amount of physical memory requested for parallel decoding jobs.
+
+	--use-make <I>
+		Use make -j <I> to run the optimizer commands (useful on large
+		shared-memory machines where qsub is unavailable).
+
+	--workdir <dir>
+		Directory for intermediate and output files.  If not specified, the
+		name is derived from the ini filename.  Assuming that the ini
+		filename begins with the decoder name and ends with ini, the default
+		name of the working directory is inferred from the middle part of
+		the filename.  E.g. an ini file named decoder.foo.ini would have
+		a default working directory name foo.
+
+Regularization options:
+
+	--tune-regularizer
+		Hold out one third of the tuning data and used this to tune the
+		regularization parameter.
+
+	--reg <F>
+
+Help
+}
+
+sub convert {
+  my ($str) = @_;
+  my @ps = split /;/, $str;
+  my %dict = ();
+  for my $p (@ps) {
+    my ($k, $v) = split /=/, $p;
+    $dict{$k} = $v;
+  }
+  return %dict;
+}
+
+
+sub cmdline {
+    return join ' ',($0,@ORIG_ARGV);
+}
+
+#buggy: last arg gets quoted sometimes?
+my $is_shell_special=qr{[ \t\n\\><|&;"'`~*?{}$!()]};
+my $shell_escape_in_quote=qr{[\\"\$`!]};
+
+sub escape_shell {
+    my ($arg)=@_;
+    return undef unless defined $arg;
+    if ($arg =~ /$is_shell_special/) {
+        $arg =~ s/($shell_escape_in_quote)/\\$1/g;
+        return "\"$arg\"";
+    }
+    return $arg;
+}
+
+sub escaped_shell_args {
+    return map {local $_=$_;chomp;escape_shell($_)} @_;
+}
+
+sub escaped_shell_args_str {
+    return join ' ',&escaped_shell_args(@_);
+}
+
+sub escaped_cmdline {
+    return "$0 ".&escaped_shell_args_str(@ORIG_ARGV);
+}
diff --git a/pro-train/mr_pro_generate_mapper_input.pl b/pro-train/mr_pro_generate_mapper_input.pl
new file mode 100755
index 00000000..b30fc4fd
--- /dev/null
+++ b/pro-train/mr_pro_generate_mapper_input.pl
@@ -0,0 +1,18 @@
+#!/usr/bin/perl -w
+use strict;
+
+die "Usage: $0 HG_DIR\n" unless scalar @ARGV == 1;
+my $d = shift @ARGV;
+die "Can't find directory $d" unless -d $d;
+
+opendir(DIR, $d) or die "Can't read $d: $!";
+my @hgs = grep { /\.gz$/ } readdir(DIR);
+closedir DIR;
+
+for my $hg (@hgs) {
+  my $file = $hg;
+  my $id = $hg;
+  $id =~ s/(\.json)?\.gz//;
+  print "$d/$file $id\n";
+}
+
diff --git a/pro-train/mr_pro_map.cc b/pro-train/mr_pro_map.cc
new file mode 100644
index 00000000..0a9b75d7
--- /dev/null
+++ b/pro-train/mr_pro_map.cc
@@ -0,0 +1,347 @@
+#include <sstream>
+#include <iostream>
+#include <fstream>
+#include <vector>
+#include <tr1/unordered_map>
+
+#include <boost/functional/hash.hpp>
+#include <boost/shared_ptr.hpp>
+#include <boost/program_options.hpp>
+#include <boost/program_options/variables_map.hpp>
+
+#include "sampler.h"
+#include "filelib.h"
+#include "stringlib.h"
+#include "weights.h"
+#include "scorer.h"
+#include "inside_outside.h"
+#include "hg_io.h"
+#include "kbest.h"
+#include "viterbi.h"
+
+// This is Figure 4 (Algorithm Sampler) from Hopkins&May (2011)
+
+using namespace std;
+namespace po = boost::program_options;
+
+struct ApproxVectorHasher {
+  static const size_t MASK = 0xFFFFFFFFull;
+  union UType {
+    double f;   // leave as double
+    size_t i;
+  };
+  static inline double round(const double x) {
+    UType t;
+    t.f = x;
+    size_t r = t.i & MASK;
+    if ((r << 1) > MASK)
+      t.i += MASK - r + 1;
+    else
+      t.i &= (1ull - MASK);
+    return t.f;
+  }
+  size_t operator()(const SparseVector<weight_t>& x) const {
+    size_t h = 0x573915839;
+    for (SparseVector<weight_t>::const_iterator it = x.begin(); it != x.end(); ++it) {
+      UType t;
+      t.f = it->second;
+      if (t.f) {
+        size_t z = (t.i >> 32);
+        boost::hash_combine(h, it->first);
+        boost::hash_combine(h, z);
+      }
+    }
+    return h;
+  }
+};
+
+struct ApproxVectorEquals {
+  bool operator()(const SparseVector<weight_t>& a, const SparseVector<weight_t>& b) const {
+    SparseVector<weight_t>::const_iterator bit = b.begin();
+    for (SparseVector<weight_t>::const_iterator ait = a.begin(); ait != a.end(); ++ait) {
+      if (bit == b.end() ||
+          ait->first != bit->first ||
+          ApproxVectorHasher::round(ait->second) != ApproxVectorHasher::round(bit->second))
+        return false;
+      ++bit;
+    }
+    if (bit != b.end()) return false;
+    return true;
+  }
+};
+
+boost::shared_ptr<MT19937> rng;
+
+void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
+  po::options_description opts("Configuration options");
+  opts.add_options()
+        ("reference,r",po::value<vector<string> >(), "[REQD] Reference translation (tokenized text)")
+        ("weights,w",po::value<string>(), "[REQD] Weights files from current iterations")
+        ("kbest_repository,K",po::value<string>()->default_value("./kbest"),"K-best list repository (directory)")
+        ("input,i",po::value<string>()->default_value("-"), "Input file to map (- is STDIN)")
+        ("source,s",po::value<string>()->default_value(""), "Source file (ignored, except for AER)")
+        ("loss_function,l",po::value<string>()->default_value("ibm_bleu"), "Loss function being optimized")
+        ("kbest_size,k",po::value<unsigned>()->default_value(1500u), "Top k-hypotheses to extract")
+        ("candidate_pairs,G", po::value<unsigned>()->default_value(5000u), "Number of pairs to sample per hypothesis (Gamma)")
+        ("best_pairs,X", po::value<unsigned>()->default_value(50u), "Number of pairs, ranked by magnitude of objective delta, to retain (Xi)")
+        ("random_seed,S", po::value<uint32_t>(), "Random seed (if not specified, /dev/random will be used)")
+        ("help,h", "Help");
+  po::options_description dcmdline_options;
+  dcmdline_options.add(opts);
+  po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
+  bool flag = false;
+  if (!conf->count("reference")) {
+    cerr << "Please specify one or more references using -r <REF.TXT>\n";
+    flag = true;
+  }
+  if (!conf->count("weights")) {
+    cerr << "Please specify weights using -w <WEIGHTS.TXT>\n";
+    flag = true;
+  }
+  if (flag || conf->count("help")) {
+    cerr << dcmdline_options << endl;
+    exit(1);
+  }
+}
+
+struct HypInfo {
+  HypInfo() : g_(-100.0f) {}
+  HypInfo(const vector<WordID>& h, const SparseVector<weight_t>& feats) : hyp(h), g_(-100.0f), x(feats) {}
+
+  // lazy evaluation
+  double g(const SentenceScorer& scorer) const {
+    if (g_ == -100.0f)
+      g_ = scorer.ScoreCandidate(hyp)->ComputeScore();
+    return g_;
+  }
+  vector<WordID> hyp;
+  mutable float g_;
+  SparseVector<weight_t> x;
+};
+
+struct HypInfoCompare {
+  bool operator()(const HypInfo& a, const HypInfo& b) const {
+    ApproxVectorEquals comp;
+    return (a.hyp == b.hyp && comp(a.x,b.x));
+  }
+};
+
+struct HypInfoHasher {
+  size_t operator()(const HypInfo& x) const {
+    boost::hash<vector<WordID> > hhasher;
+    ApproxVectorHasher vhasher;
+    size_t ha = hhasher(x.hyp);
+    boost::hash_combine(ha, vhasher(x.x));
+    return ha;
+  }
+};
+
+void WriteKBest(const string& file, const vector<HypInfo>& kbest) {
+  WriteFile wf(file);
+  ostream& out = *wf.stream();
+  out.precision(10);
+  for (int i = 0; i < kbest.size(); ++i) {
+    out << TD::GetString(kbest[i].hyp) << endl;
+    out << kbest[i].x << endl;
+  }
+}
+
+void ParseSparseVector(string& line, size_t cur, SparseVector<weight_t>* out) {
+  SparseVector<weight_t>& x = *out;
+  size_t last_start = cur;
+  size_t last_comma = string::npos;
+  while(cur <= line.size()) {
+    if (line[cur] == ' ' || cur == line.size()) {
+      if (!(cur > last_start && last_comma != string::npos && cur > last_comma)) {
+        cerr << "[ERROR] " << line << endl << "  position = " << cur << endl;
+        exit(1);
+      }
+      const int fid = FD::Convert(line.substr(last_start, last_comma - last_start));
+      if (cur < line.size()) line[cur] = 0;
+      const double val = strtod(&line[last_comma + 1], NULL);
+      x.set_value(fid, val);
+
+      last_comma = string::npos;
+      last_start = cur+1;
+    } else {
+      if (line[cur] == '=')
+        last_comma = cur;
+    }
+    ++cur;
+  }
+}
+
+void ReadKBest(const string& file, vector<HypInfo>* kbest) {
+  cerr << "Reading from " << file << endl;
+  ReadFile rf(file);
+  istream& in = *rf.stream();
+  string cand;
+  string feats;
+  while(getline(in, cand)) {
+    getline(in, feats);
+    assert(in);
+    kbest->push_back(HypInfo());
+    TD::ConvertSentence(cand, &kbest->back().hyp);
+    ParseSparseVector(feats, 0, &kbest->back().x);
+  }
+  cerr << "  read " << kbest->size() << " hypotheses\n";
+}
+
+void Dedup(vector<HypInfo>* h) {
+  cerr << "Dedup in=" << h->size();
+  tr1::unordered_set<HypInfo, HypInfoHasher, HypInfoCompare> u;
+  while(h->size() > 0) {
+    u.insert(h->back());
+    h->pop_back();
+  }
+  tr1::unordered_set<HypInfo, HypInfoHasher, HypInfoCompare>::iterator it = u.begin();
+  while (it != u.end()) {
+    h->push_back(*it);
+    it = u.erase(it);
+  }
+  cerr << "  out=" << h->size() << endl;
+}
+
+struct ThresholdAlpha {
+  explicit ThresholdAlpha(double t = 0.05) : threshold(t) {}
+  double operator()(double mag) const {
+    if (mag < threshold) return 0.0; else return 1.0;
+  }
+  const double threshold;
+};
+
+struct TrainingInstance {
+  TrainingInstance(const SparseVector<weight_t>& feats, bool positive, float diff) : x(feats), y(positive), gdiff(diff) {}
+  SparseVector<weight_t> x;
+#undef DEBUGGING_PRO
+#ifdef DEBUGGING_PRO
+  vector<WordID> a;
+  vector<WordID> b;
+#endif
+  bool y;
+  float gdiff;
+};
+#ifdef DEBUGGING_PRO
+ostream& operator<<(ostream& os, const TrainingInstance& d) {
+  return os << d.gdiff << " y=" << d.y << "\tA:" << TD::GetString(d.a) << "\n\tB: " << TD::GetString(d.b) << "\n\tX: " << d.x;
+}
+#endif
+
+struct DiffOrder {
+  bool operator()(const TrainingInstance& a, const TrainingInstance& b) const {
+    return a.gdiff > b.gdiff;
+  }
+};
+
+void Sample(const unsigned gamma, const unsigned xi, const vector<HypInfo>& J_i, const SentenceScorer& scorer, const bool invert_score, vector<TrainingInstance>* pv) {
+  vector<TrainingInstance> v1, v2;
+  float avg_diff = 0;
+  for (unsigned i = 0; i < gamma; ++i) {
+    const size_t a = rng->inclusive(0, J_i.size() - 1)();
+    const size_t b = rng->inclusive(0, J_i.size() - 1)();
+    if (a == b) continue;
+    float ga = J_i[a].g(scorer);
+    float gb = J_i[b].g(scorer);
+    bool positive = gb < ga;
+    if (invert_score) positive = !positive;
+    const float gdiff = fabs(ga - gb);
+    if (!gdiff) continue;
+    avg_diff += gdiff;
+    SparseVector<weight_t> xdiff = (J_i[a].x - J_i[b].x).erase_zeros();
+    if (xdiff.empty()) {
+      cerr << "Empty diff:\n  " << TD::GetString(J_i[a].hyp) << endl << "x=" << J_i[a].x << endl;
+      cerr << "  " << TD::GetString(J_i[b].hyp) << endl << "x=" << J_i[b].x << endl;
+      continue;
+    }
+    v1.push_back(TrainingInstance(xdiff, positive, gdiff));
+#ifdef DEBUGGING_PRO
+    v1.back().a = J_i[a].hyp;
+    v1.back().b = J_i[b].hyp;
+    cerr << "N: " << v1.back() << endl;
+#endif
+  }
+  avg_diff /= v1.size();
+
+  for (unsigned i = 0; i < v1.size(); ++i) {
+    double p = 1.0 / (1.0 + exp(-avg_diff - v1[i].gdiff));
+    // cerr << "avg_diff=" << avg_diff << "  gdiff=" << v1[i].gdiff << "  p=" << p << endl;
+    if (rng->next() < p) v2.push_back(v1[i]);
+  }
+  vector<TrainingInstance>::iterator mid = v2.begin() + xi;
+  if (xi > v2.size()) mid = v2.end();
+  partial_sort(v2.begin(), mid, v2.end(), DiffOrder());
+  copy(v2.begin(), mid, back_inserter(*pv));
+#ifdef DEBUGGING_PRO
+  if (v2.size() >= 5) {
+    for (int i =0; i < (mid - v2.begin()); ++i) {
+      cerr << v2[i] << endl;
+    }
+    cerr << pv->back() << endl;
+  }
+#endif
+}
+
+int main(int argc, char** argv) {
+  po::variables_map conf;
+  InitCommandLine(argc, argv, &conf);
+  if (conf.count("random_seed"))
+    rng.reset(new MT19937(conf["random_seed"].as<uint32_t>()));
+  else
+    rng.reset(new MT19937);
+  const string loss_function = conf["loss_function"].as<string>();
+
+  ScoreType type = ScoreTypeFromString(loss_function);
+  DocScorer ds(type, conf["reference"].as<vector<string> >(), conf["source"].as<string>());
+  cerr << "Loaded " << ds.size() << " references for scoring with " << loss_function << endl;
+  Hypergraph hg;
+  string last_file;
+  ReadFile in_read(conf["input"].as<string>());
+  istream &in=*in_read.stream();
+  const unsigned kbest_size = conf["kbest_size"].as<unsigned>();
+  const unsigned gamma = conf["candidate_pairs"].as<unsigned>();
+  const unsigned xi = conf["best_pairs"].as<unsigned>();
+  string weightsf = conf["weights"].as<string>();
+  vector<weight_t> weights;
+  Weights::InitFromFile(weightsf, &weights);
+  string kbest_repo = conf["kbest_repository"].as<string>();
+  MkDirP(kbest_repo);
+  while(in) {
+    vector<TrainingInstance> v;
+    string line;
+    getline(in, line);
+    if (line.empty()) continue;
+    istringstream is(line);
+    int sent_id;
+    string file;
+    // path-to-file (JSON) sent_id
+    is >> file >> sent_id;
+    ReadFile rf(file);
+    ostringstream os;
+    vector<HypInfo> J_i;
+    os << kbest_repo << "/kbest." << sent_id << ".txt.gz";
+    const string kbest_file = os.str();
+    if (FileExists(kbest_file))
+      ReadKBest(kbest_file, &J_i);
+    HypergraphIO::ReadFromJSON(rf.stream(), &hg);
+    hg.Reweight(weights);
+    KBest::KBestDerivations<vector<WordID>, ESentenceTraversal> kbest(hg, kbest_size);
+
+    for (int i = 0; i < kbest_size; ++i) {
+      const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal>::Derivation* d =
+        kbest.LazyKthBest(hg.nodes_.size() - 1, i);
+      if (!d) break;
+      J_i.push_back(HypInfo(d->yield, d->feature_values));
+    }
+    Dedup(&J_i);
+    WriteKBest(kbest_file, J_i);
+
+    Sample(gamma, xi, J_i, *ds[sent_id], (type == TER), &v);
+    for (unsigned i = 0; i < v.size(); ++i) {
+      const TrainingInstance& vi = v[i];
+      cout << vi.y << "\t" << vi.x << endl;
+      cout << (!vi.y) << "\t" << (vi.x * -1.0) << endl;
+    }
+  }
+  return 0;
+}
+
diff --git a/pro-train/mr_pro_reduce.cc b/pro-train/mr_pro_reduce.cc
new file mode 100644
index 00000000..aff410a0
--- /dev/null
+++ b/pro-train/mr_pro_reduce.cc
@@ -0,0 +1,279 @@
+#include <cstdlib>
+#include <sstream>
+#include <iostream>
+#include <fstream>
+#include <vector>
+
+#include <boost/program_options.hpp>
+#include <boost/program_options/variables_map.hpp>
+
+#include "filelib.h"
+#include "weights.h"
+#include "sparse_vector.h"
+#include "optimize.h"
+
+using namespace std;
+namespace po = boost::program_options;
+
+// since this is a ranking model, there should be equal numbers of
+// positive and negative examples, so the bias should be 0
+static const double MAX_BIAS = 1e-10;
+
+void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
+  po::options_description opts("Configuration options");
+  opts.add_options()
+        ("weights,w", po::value<string>(), "Weights from previous iteration (used as initialization and interpolation")
+        ("interpolation,p",po::value<double>()->default_value(0.9), "Output weights are p*w + (1-p)*w_prev")
+        ("memory_buffers,m",po::value<unsigned>()->default_value(200), "Number of memory buffers (LBFGS)")
+        ("sigma_squared,s",po::value<double>()->default_value(0.1), "Sigma squared for Gaussian prior")
+        ("min_reg,r",po::value<double>()->default_value(1e-8), "When tuning (-T) regularization strength, minimum regularization strenght")
+        ("max_reg,R",po::value<double>()->default_value(10.0), "When tuning (-T) regularization strength, maximum regularization strenght")
+        ("testset,t",po::value<string>(), "Optional held-out test set")
+        ("tune_regularizer,T", "Use the held out test set (-t) to tune the regularization strength")
+        ("help,h", "Help");
+  po::options_description dcmdline_options;
+  dcmdline_options.add(opts);
+  po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
+  if (conf->count("help")) {
+    cerr << dcmdline_options << endl;
+    exit(1);
+  }
+}
+
+void ParseSparseVector(string& line, size_t cur, SparseVector<weight_t>* out) {
+  SparseVector<weight_t>& x = *out;
+  size_t last_start = cur;
+  size_t last_comma = string::npos;
+  while(cur <= line.size()) {
+    if (line[cur] == ' ' || cur == line.size()) {
+      if (!(cur > last_start && last_comma != string::npos && cur > last_comma)) {
+        cerr << "[ERROR] " << line << endl << "  position = " << cur << endl;
+        exit(1);
+      }
+      const int fid = FD::Convert(line.substr(last_start, last_comma - last_start));
+      if (cur < line.size()) line[cur] = 0;
+      const weight_t val = strtod(&line[last_comma + 1], NULL);
+      x.set_value(fid, val);
+
+      last_comma = string::npos;
+      last_start = cur+1;
+    } else {
+      if (line[cur] == '=')
+        last_comma = cur;
+    }
+    ++cur;
+  }
+}
+
+void ReadCorpus(istream* pin, vector<pair<bool, SparseVector<weight_t> > >* corpus) {
+  istream& in = *pin;
+  corpus->clear();
+  bool flag = false;
+  int lc = 0;
+  string line;
+  SparseVector<weight_t> x;
+  while(getline(in, line)) {
+    ++lc;
+    if (lc % 1000 == 0) { cerr << '.'; flag = true; }
+    if (lc % 40000 == 0) { cerr << " [" << lc << "]\n"; flag = false; }
+    if (line.empty()) continue;
+    const size_t ks = line.find("\t");
+    assert(string::npos != ks);
+    assert(ks == 1);
+    const bool y = line[0] == '1';
+    x.clear();
+    ParseSparseVector(line, ks + 1, &x);
+    corpus->push_back(make_pair(y, x));
+  }
+  if (flag) cerr << endl;
+}
+
+void GradAdd(const SparseVector<weight_t>& v, const double scale, vector<weight_t>* acc) {
+  for (SparseVector<weight_t>::const_iterator it = v.begin();
+       it != v.end(); ++it) {
+    (*acc)[it->first] += it->second * scale;
+  }
+}
+
+double TrainingInference(const vector<weight_t>& x,
+                         const vector<pair<bool, SparseVector<weight_t> > >& corpus,
+                         vector<weight_t>* g = NULL) {
+  double cll = 0;
+  for (int i = 0; i < corpus.size(); ++i) {
+    const double dotprod = corpus[i].second.dot(x) + (x.size() ? x[0] : weight_t()); // x[0] is bias
+    double lp_false = dotprod;
+    double lp_true = -dotprod;
+    if (0 < lp_true) {
+      lp_true += log1p(exp(-lp_true));
+      lp_false = log1p(exp(lp_false));
+    } else {
+      lp_true = log1p(exp(lp_true));
+      lp_false += log1p(exp(-lp_false));
+    }
+    lp_true*=-1;
+    lp_false*=-1;
+    if (corpus[i].first) {  // true label
+      cll -= lp_true;
+      if (g) {
+        // g -= corpus[i].second * exp(lp_false);
+        GradAdd(corpus[i].second, -exp(lp_false), g);
+        (*g)[0] -= exp(lp_false); // bias
+      }
+    } else {                  // false label
+      cll -= lp_false;
+      if (g) {
+        // g += corpus[i].second * exp(lp_true);
+        GradAdd(corpus[i].second, exp(lp_true), g);
+        (*g)[0] += exp(lp_true); // bias
+      }
+    }
+  }
+  return cll;
+}
+
+// return held-out log likelihood
+double LearnParameters(const vector<pair<bool, SparseVector<weight_t> > >& training,
+                       const vector<pair<bool, SparseVector<weight_t> > >& testing,
+                       const double sigsq,
+                       const unsigned memory_buffers,
+                       vector<weight_t>* px) {
+  vector<weight_t>& x = *px;
+  vector<weight_t> vg(FD::NumFeats(), 0.0);
+  bool converged = false;
+  LBFGSOptimizer opt(FD::NumFeats(), memory_buffers);
+  double tppl = 0.0;
+  while(!converged) {
+    fill(vg.begin(), vg.end(), 0.0);
+    double cll = TrainingInference(x, training, &vg);
+    double ppl = cll / log(2);
+    ppl /= training.size();
+    ppl = pow(2.0, ppl);
+
+    // evaluate optional held-out test set
+    if (testing.size()) {
+      tppl = TrainingInference(x, testing) / log(2);
+      tppl /= testing.size();
+      tppl = pow(2.0, tppl);
+    }
+
+    // handle regularizer
+#if 1
+    double norm = 0;
+    for (int i = 1; i < x.size(); ++i) {
+      const double mean_i = 0.0;
+      const double param = (x[i] - mean_i);
+      norm += param * param;
+      vg[i] += param / sigsq;
+    } 
+    const double reg = norm / (2.0 * sigsq);
+#else
+    double reg = 0;
+#endif
+    cll += reg;
+    cerr << cll << " (REG=" << reg << ")\tPPL=" << ppl << "\t TEST_PPL=" << tppl << "\t";
+    try {
+      vector<weight_t> old_x = x;
+      do {
+        opt.Optimize(cll, vg, &x);
+        converged = opt.HasConverged();
+      } while (!converged && x == old_x);
+    } catch (...) {
+      cerr << "Exception caught, assuming convergence is close enough...\n";
+      converged = true;
+    }
+    if (fabs(x[0]) > MAX_BIAS) {
+      cerr << "Biased model learned. Are your training instances wrong?\n";
+      cerr << "  BIAS: " << x[0] << endl;
+    }
+  }
+  return tppl;
+}
+
+int main(int argc, char** argv) {
+  po::variables_map conf;
+  InitCommandLine(argc, argv, &conf);
+  string line;
+  vector<pair<bool, SparseVector<weight_t> > > training, testing;
+  const bool tune_regularizer = conf.count("tune_regularizer");
+  if (tune_regularizer && !conf.count("testset")) {
+    cerr << "--tune_regularizer requires --testset to be set\n";
+    return 1;
+  }
+  const double min_reg = conf["min_reg"].as<double>();
+  const double max_reg = conf["max_reg"].as<double>();
+  double sigsq = conf["sigma_squared"].as<double>(); // will be overridden if parameter is tuned
+  assert(sigsq > 0.0);
+  assert(min_reg > 0.0);
+  assert(max_reg > 0.0);
+  assert(max_reg > min_reg);
+  const double psi = conf["interpolation"].as<double>();
+  if (psi < 0.0 || psi > 1.0) { cerr << "Invalid interpolation weight: " << psi << endl; }
+  ReadCorpus(&cin, &training);
+  if (conf.count("testset")) {
+    ReadFile rf(conf["testset"].as<string>());
+    ReadCorpus(rf.stream(), &testing);
+  }
+  cerr << "Number of features: " << FD::NumFeats() << endl;
+
+  vector<weight_t> x, prev_x;  // x[0] is bias
+  if (conf.count("weights")) {
+    Weights::InitFromFile(conf["weights"].as<string>(), &x);
+    x.resize(FD::NumFeats());
+    prev_x = x;
+  } else {
+    x.resize(FD::NumFeats());
+    prev_x = x;
+  }
+  cerr << "         Number of features: " << x.size() << endl;
+  cerr << "Number of training examples: " << training.size() << endl;
+  cerr << "Number of  testing examples: " << testing.size() << endl;
+  double tppl = 0.0;
+  vector<pair<double,double> > sp;
+  vector<double> smoothed;
+  if (tune_regularizer) {
+    sigsq = min_reg;
+    const double steps = 18;
+    double sweep_factor = exp((log(max_reg) - log(min_reg)) / steps);
+    cerr << "SWEEP FACTOR: " << sweep_factor << endl;
+    while(sigsq < max_reg) {
+      tppl = LearnParameters(training, testing, sigsq, conf["memory_buffers"].as<unsigned>(), &x);
+      sp.push_back(make_pair(sigsq, tppl));
+      sigsq *= sweep_factor;
+    }
+    smoothed.resize(sp.size(), 0);
+    smoothed[0] = sp[0].second;
+    smoothed.back() = sp.back().second; 
+    for (int i = 1; i < sp.size()-1; ++i) {
+      double prev = sp[i-1].second;
+      double next = sp[i+1].second;
+      double cur = sp[i].second;
+      smoothed[i] = (prev*0.2) + cur * 0.6 + (0.2*next);
+    }
+    double best_ppl = 9999999;
+    unsigned best_i = 0;
+    for (unsigned i = 0; i < sp.size(); ++i) {
+      if (smoothed[i] < best_ppl) {
+        best_ppl = smoothed[i];
+        best_i = i;
+      }
+    }
+    sigsq = sp[best_i].first;
+  }  // tune regularizer
+  tppl = LearnParameters(training, testing, sigsq, conf["memory_buffers"].as<unsigned>(), &x);
+  if (conf.count("weights")) {
+    for (int i = 1; i < x.size(); ++i) {
+      x[i] = (x[i] * psi) + prev_x[i] * (1.0 - psi);
+    }
+  }
+  cout.precision(15);
+  cout << "# sigma^2=" << sigsq << "\theld out perplexity=";
+  if (tppl) { cout << tppl << endl; } else { cout << "N/A\n"; }
+  if (sp.size()) {
+    cout << "# Parameter sweep:\n";
+    for (int i = 0; i < sp.size(); ++i) {
+      cout << "# " << sp[i].first << "\t" << sp[i].second << "\t" << smoothed[i] << endl;
+    }
+  }
+  Weights::WriteToFile("-", x);
+  return 0;
+}