From 1b8181bf0d6e9137e6b9ccdbe414aec37377a1a9 Mon Sep 17 00:00:00 2001 From: Chris Dyer Date: Sun, 18 Nov 2012 13:35:42 -0500 Subject: major restructure of the training code --- dtrain/hstreaming/avg.rb | 32 -------- dtrain/hstreaming/cdec.ini | 22 ----- dtrain/hstreaming/dtrain.ini | 15 ---- dtrain/hstreaming/dtrain.sh | 9 -- dtrain/hstreaming/hadoop-streaming-job.sh | 30 ------- dtrain/hstreaming/lplp.rb | 131 ------------------------------ dtrain/hstreaming/red-test | 9 -- 7 files changed, 248 deletions(-) delete mode 100755 dtrain/hstreaming/avg.rb delete mode 100644 dtrain/hstreaming/cdec.ini delete mode 100644 dtrain/hstreaming/dtrain.ini delete mode 100755 dtrain/hstreaming/dtrain.sh delete mode 100755 dtrain/hstreaming/hadoop-streaming-job.sh delete mode 100755 dtrain/hstreaming/lplp.rb delete mode 100644 dtrain/hstreaming/red-test (limited to 'dtrain/hstreaming') diff --git a/dtrain/hstreaming/avg.rb b/dtrain/hstreaming/avg.rb deleted file mode 100755 index 2599c732..00000000 --- a/dtrain/hstreaming/avg.rb +++ /dev/null @@ -1,32 +0,0 @@ -#!/usr/bin/env ruby -# first arg may be an int of custom shard count - -shard_count_key = "__SHARD_COUNT__" - -STDIN.set_encoding 'utf-8' -STDOUT.set_encoding 'utf-8' - -w = {} -c = {} -w.default = 0 -c.default = 0 -while line = STDIN.gets - key, val = line.split /\s/ - w[key] += val.to_f - c[key] += 1 -end - -if ARGV.size == 0 - shard_count = w["__SHARD_COUNT__"] -else - shard_count = ARGV[0].to_f -end -w.each_key { |k| - if k == shard_count_key - next - else - puts "#{k}\t#{w[k]/shard_count}" - #puts "# #{c[k]}" - end -} - diff --git a/dtrain/hstreaming/cdec.ini b/dtrain/hstreaming/cdec.ini deleted file mode 100644 index d4f5cecd..00000000 --- a/dtrain/hstreaming/cdec.ini +++ /dev/null @@ -1,22 +0,0 @@ -formalism=scfg -add_pass_through_rules=true -scfg_max_span_limit=15 -intersection_strategy=cube_pruning -cubepruning_pop_limit=30 -feature_function=WordPenalty -feature_function=KLanguageModel nc-wmt11.en.srilm.gz -#feature_function=ArityPenalty -#feature_function=CMR2008ReorderingFeatures -#feature_function=Dwarf -#feature_function=InputIndicator -#feature_function=LexNullJump -#feature_function=NewJump -#feature_function=NgramFeatures -#feature_function=NonLatinCount -#feature_function=OutputIndicator -#feature_function=RuleIdentityFeatures -#feature_function=RuleNgramFeatures -#feature_function=RuleShape -#feature_function=SourceSpanSizeFeatures -#feature_function=SourceWordPenalty -#feature_function=SpanFeatures diff --git a/dtrain/hstreaming/dtrain.ini b/dtrain/hstreaming/dtrain.ini deleted file mode 100644 index a2c219a1..00000000 --- a/dtrain/hstreaming/dtrain.ini +++ /dev/null @@ -1,15 +0,0 @@ -input=- -output=- -decoder_config=cdec.ini -tmp=/var/hadoop/mapred/local/ -epochs=1 -k=100 -N=4 -learning_rate=0.0001 -gamma=0 -scorer=stupid_bleu -sample_from=kbest -filter=uniq -pair_sampling=XYX -pair_threshold=0 -select_weights=last diff --git a/dtrain/hstreaming/dtrain.sh b/dtrain/hstreaming/dtrain.sh deleted file mode 100755 index 877ff94c..00000000 --- a/dtrain/hstreaming/dtrain.sh +++ /dev/null @@ -1,9 +0,0 @@ -#!/bin/bash -# script to run dtrain with a task id - -pushd . &>/dev/null -cd .. -ID=$(basename $(pwd)) # attempt_... -popd &>/dev/null -./dtrain -c dtrain.ini --hstreaming $ID - diff --git a/dtrain/hstreaming/hadoop-streaming-job.sh b/dtrain/hstreaming/hadoop-streaming-job.sh deleted file mode 100755 index 92419956..00000000 --- a/dtrain/hstreaming/hadoop-streaming-job.sh +++ /dev/null @@ -1,30 +0,0 @@ -#!/bin/sh - -EXP=a_simple_test - -# change these vars to fit your hadoop installation -HADOOP_HOME=/usr/lib/hadoop-0.20 -JAR=contrib/streaming/hadoop-streaming-0.20.2-cdh3u1.jar -HSTREAMING="$HADOOP_HOME/bin/hadoop jar $HADOOP_HOME/$JAR" - - IN=input_on_hdfs -OUT=output_weights_on_hdfs - -# you can -reducer to NONE if you want to -# do feature selection/averaging locally (e.g. to -# keep weights of all epochs) -$HSTREAMING \ - -mapper "dtrain.sh" \ - -reducer "ruby lplp.rb l2 select_k 100000" \ - -input $IN \ - -output $OUT \ - -file dtrain.sh \ - -file lplp.rb \ - -file ../dtrain \ - -file dtrain.ini \ - -file cdec.ini \ - -file ../test/example/nc-wmt11.en.srilm.gz \ - -jobconf mapred.reduce.tasks=30 \ - -jobconf mapred.max.map.failures.percent=0 \ - -jobconf mapred.job.name="dtrain $EXP" - diff --git a/dtrain/hstreaming/lplp.rb b/dtrain/hstreaming/lplp.rb deleted file mode 100755 index f0cd58c5..00000000 --- a/dtrain/hstreaming/lplp.rb +++ /dev/null @@ -1,131 +0,0 @@ -# lplp.rb - -# norms -def l0(feature_column, n) - if feature_column.size >= n then return 1 else return 0 end -end - -def l1(feature_column, n=-1) - return feature_column.map { |i| i.abs }.reduce { |sum,i| sum+i } -end - -def l2(feature_column, n=-1) - return Math.sqrt feature_column.map { |i| i.abs2 }.reduce { |sum,i| sum+i } -end - -def linfty(feature_column, n=-1) - return feature_column.map { |i| i.abs }.max -end - -# stats -def median(feature_column, n) - return feature_column.concat(0.step(n-feature_column.size-1).map{|i|0}).sort[feature_column.size/2] -end - -def mean(feature_column, n) - return feature_column.reduce { |sum, i| sum+i } / n -end - -# selection -def select_k(weights, norm_fun, n, k=10000) - weights.sort{|a,b| norm_fun.call(b[1], n) <=> norm_fun.call(a[1], n)}.each { |p| - puts "#{p[0]}\t#{mean(p[1], n)}" - k -= 1 - if k == 0 then break end - } -end - -def cut(weights, norm_fun, n, epsilon=0.0001) - weights.each { |k,v| - if norm_fun.call(v, n).abs >= epsilon - puts "#{k}\t#{mean(v, n)}" - end - } -end - -# test -def _test() - puts - w = {} - w["a"] = [1, 2, 3] - w["b"] = [1, 2] - w["c"] = [66] - w["d"] = [10, 20, 30] - n = 3 - puts w.to_s - puts - puts "select_k" - puts "l0 expect ad" - select_k(w, method(:l0), n, 2) - puts "l1 expect cd" - select_k(w, method(:l1), n, 2) - puts "l2 expect c" - select_k(w, method(:l2), n, 1) - puts - puts "cut" - puts "l1 expect cd" - cut(w, method(:l1), n, 7) - puts - puts "median" - a = [1,2,3,4,5] - puts a.to_s - puts median(a, 5) - puts - puts "#{median(a, 7)} <- that's because we add missing 0s:" - puts a.concat(0.step(7-a.size-1).map{|i|0}).to_s - puts - puts "mean expect bc" - w.clear - w["a"] = [2] - w["b"] = [2.1] - w["c"] = [2.2] - cut(w, method(:mean), 1, 2.05) - exit -end -#_test() - -# actually do something -def usage() - puts "lplp.rb [n] < " - puts " l0...: norms for selection" - puts "select_k: only output top k (according to the norm of their column vector) features" - puts " cut: output features with weight >= threshold" - puts " n: if we do not have a shard count use this number for averaging" - exit -end - -if ARGV.size < 3 then usage end -norm_fun = method(ARGV[0].to_sym) -type = ARGV[1] -x = ARGV[2].to_f - -shard_count_key = "__SHARD_COUNT__" - -STDIN.set_encoding 'utf-8' -STDOUT.set_encoding 'utf-8' - -w = {} -shard_count = 0 -while line = STDIN.gets - key, val = line.split /\s+/ - if key == shard_count_key - shard_count += 1 - next - end - if w.has_key? key - w[key].push val.to_f - else - w[key] = [val.to_f] - end -end - -if ARGV.size == 4 then shard_count = ARGV[3].to_f end - -if type == 'cut' - cut(w, norm_fun, shard_count, x) -elsif type == 'select_k' - select_k(w, norm_fun, shard_count, x) -else - puts "oh oh" -end - diff --git a/dtrain/hstreaming/red-test b/dtrain/hstreaming/red-test deleted file mode 100644 index 2623d697..00000000 --- a/dtrain/hstreaming/red-test +++ /dev/null @@ -1,9 +0,0 @@ -a 1 -b 2 -c 3.5 -a 1 -b 2 -c 3.5 -d 1 -e 2 -__SHARD_COUNT__ 2 -- cgit v1.2.3