major restructure of the training code

7 files changed, 0 insertions, 248 deletions

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 <l0,l1,l2,linfty,mean,median> <cut|select_k> <k|threshold> [n] < <input>"
-  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