overhauled ruby scripts and examples

14 files changed, 337 insertions, 0 deletions

diff --git a/training/dtrain/examples/parallelized/work/out.0.0 b/training/dtrain/examples/parallelized/work/out.0.0
new file mode 100644
index 00000000..7a00ed0f
--- /dev/null
+++ b/training/dtrain/examples/parallelized/work/out.0.0
@@ -0,0 +1,61 @@
+                cdec cfg 'cdec.ini'
+Loading the LM will be faster if you build a binary file.
+Reading ../example/nc-wmt11.en.srilm.gz
+----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100
+****************************************************************************************************
+Seeding random number sequence to 3121929377
+
+dtrain
+Parameters:
+                       k 100
+                       N 4
+                       T 1
+                  scorer 'stupid_bleu'
+             sample from 'kbest'
+                  filter 'uniq'
+           learning rate 0.0001
+                   gamma 0
+             loss margin 1
+                   pairs 'XYX'
+                   hi lo 0.1
+          pair threshold 0
+          select weights 'last'
+                  l1 reg 0 'none'
+               max pairs 4294967295
+                cdec cfg 'cdec.ini'
+                   input 'work/shard.0.0.in'
+                    refs 'work/shard.0.0.refs'
+                  output 'work/weights.0.0'
+(a dot represents 10 inputs)
+Iteration #1 of 1.
+  5
+WEIGHTS
+              Glue = +0.2663
+       WordPenalty = -0.0079042
+     LanguageModel = +0.44782
+ LanguageModel_OOV = -0.0401
+     PhraseModel_0 = -0.193
+     PhraseModel_1 = +0.71321
+     PhraseModel_2 = +0.85196
+     PhraseModel_3 = -0.43986
+     PhraseModel_4 = -0.44803
+     PhraseModel_5 = -0.0538
+     PhraseModel_6 = -0.1788
+       PassThrough = -0.1477
+        ---
+       1best avg score: 0.17521 (+0.17521)
+ 1best avg model score: 21.556 (+21.556)
+           avg # pairs: 1671.2
+        avg # rank err: 1118.6
+     avg # margin viol: 552.6
+    non0 feature count: 12
+           avg list sz: 100
+           avg f count: 11.32
+(time 0.37 min, 4.4 s/S)
+
+Writing weights file to 'work/weights.0.0' ...
+done
+
+---
+Best iteration: 1 [SCORE 'stupid_bleu'=0.17521].
+This took 0.36667 min.
diff --git a/training/dtrain/examples/parallelized/work/out.0.1 b/training/dtrain/examples/parallelized/work/out.0.1
new file mode 100644
index 00000000..e2bd6649
--- /dev/null
+++ b/training/dtrain/examples/parallelized/work/out.0.1
@@ -0,0 +1,62 @@
+                cdec cfg 'cdec.ini'
+Loading the LM will be faster if you build a binary file.
+Reading ../example/nc-wmt11.en.srilm.gz
+----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100
+****************************************************************************************************
+Seeding random number sequence to 2767202922
+
+dtrain
+Parameters:
+                       k 100
+                       N 4
+                       T 1
+                  scorer 'stupid_bleu'
+             sample from 'kbest'
+                  filter 'uniq'
+           learning rate 0.0001
+                   gamma 0
+             loss margin 1
+                   pairs 'XYX'
+                   hi lo 0.1
+          pair threshold 0
+          select weights 'last'
+                  l1 reg 0 'none'
+               max pairs 4294967295
+                cdec cfg 'cdec.ini'
+                   input 'work/shard.0.0.in'
+                    refs 'work/shard.0.0.refs'
+                  output 'work/weights.0.1'
+              weights in 'work/weights.0'
+(a dot represents 10 inputs)
+Iteration #1 of 1.
+  5
+WEIGHTS
+              Glue = -0.2699
+       WordPenalty = +0.080605
+     LanguageModel = -0.026572
+ LanguageModel_OOV = -0.30025
+     PhraseModel_0 = -0.32076
+     PhraseModel_1 = +0.67451
+     PhraseModel_2 = +0.92
+     PhraseModel_3 = -0.36402
+     PhraseModel_4 = -0.592
+     PhraseModel_5 = -0.0269
+     PhraseModel_6 = -0.28755
+       PassThrough = -0.33285
+        ---
+       1best avg score: 0.26638 (+0.26638)
+ 1best avg model score: 53.197 (+53.197)
+           avg # pairs: 2028.6
+        avg # rank err: 998.2
+     avg # margin viol: 918.8
+    non0 feature count: 12
+           avg list sz: 100
+           avg f count: 10.496
+(time 0.32 min, 3.8 s/S)
+
+Writing weights file to 'work/weights.0.1' ...
+done
+
+---
+Best iteration: 1 [SCORE 'stupid_bleu'=0.26638].
+This took 0.31667 min.
diff --git a/training/dtrain/examples/parallelized/work/out.1.0 b/training/dtrain/examples/parallelized/work/out.1.0
new file mode 100644
index 00000000..6e790e38
--- /dev/null
+++ b/training/dtrain/examples/parallelized/work/out.1.0
@@ -0,0 +1,61 @@
+                cdec cfg 'cdec.ini'
+Loading the LM will be faster if you build a binary file.
+Reading ../example/nc-wmt11.en.srilm.gz
+----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100
+****************************************************************************************************
+Seeding random number sequence to 1432415010
+
+dtrain
+Parameters:
+                       k 100
+                       N 4
+                       T 1
+                  scorer 'stupid_bleu'
+             sample from 'kbest'
+                  filter 'uniq'
+           learning rate 0.0001
+                   gamma 0
+             loss margin 1
+                   pairs 'XYX'
+                   hi lo 0.1
+          pair threshold 0
+          select weights 'last'
+                  l1 reg 0 'none'
+               max pairs 4294967295
+                cdec cfg 'cdec.ini'
+                   input 'work/shard.1.0.in'
+                    refs 'work/shard.1.0.refs'
+                  output 'work/weights.1.0'
+(a dot represents 10 inputs)
+Iteration #1 of 1.
+  5
+WEIGHTS
+              Glue = -0.3815
+       WordPenalty = +0.20064
+     LanguageModel = +0.95304
+ LanguageModel_OOV = -0.264
+     PhraseModel_0 = -0.22362
+     PhraseModel_1 = +0.12254
+     PhraseModel_2 = +0.26328
+     PhraseModel_3 = +0.38018
+     PhraseModel_4 = -0.48654
+     PhraseModel_5 = +0
+     PhraseModel_6 = -0.3645
+       PassThrough = -0.2216
+        ---
+       1best avg score: 0.10863 (+0.10863)
+ 1best avg model score: -4.9841 (-4.9841)
+           avg # pairs: 1345.4
+        avg # rank err: 822.4
+     avg # margin viol: 501
+    non0 feature count: 11
+           avg list sz: 100
+           avg f count: 11.814
+(time 0.45 min, 5.4 s/S)
+
+Writing weights file to 'work/weights.1.0' ...
+done
+
+---
+Best iteration: 1 [SCORE 'stupid_bleu'=0.10863].
+This took 0.45 min.
diff --git a/training/dtrain/examples/parallelized/work/out.1.1 b/training/dtrain/examples/parallelized/work/out.1.1
new file mode 100644
index 00000000..0b984761
--- /dev/null
+++ b/training/dtrain/examples/parallelized/work/out.1.1
@@ -0,0 +1,62 @@
+                cdec cfg 'cdec.ini'
+Loading the LM will be faster if you build a binary file.
+Reading ../example/nc-wmt11.en.srilm.gz
+----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100
+****************************************************************************************************
+Seeding random number sequence to 1771918374
+
+dtrain
+Parameters:
+                       k 100
+                       N 4
+                       T 1
+                  scorer 'stupid_bleu'
+             sample from 'kbest'
+                  filter 'uniq'
+           learning rate 0.0001
+                   gamma 0
+             loss margin 1
+                   pairs 'XYX'
+                   hi lo 0.1
+          pair threshold 0
+          select weights 'last'
+                  l1 reg 0 'none'
+               max pairs 4294967295
+                cdec cfg 'cdec.ini'
+                   input 'work/shard.1.0.in'
+                    refs 'work/shard.1.0.refs'
+                  output 'work/weights.1.1'
+              weights in 'work/weights.0'
+(a dot represents 10 inputs)
+Iteration #1 of 1.
+  5
+WEIGHTS
+              Glue = -0.3178
+       WordPenalty = +0.11092
+     LanguageModel = +0.17269
+ LanguageModel_OOV = -0.13485
+     PhraseModel_0 = -0.45371
+     PhraseModel_1 = +0.38789
+     PhraseModel_2 = +0.75311
+     PhraseModel_3 = -0.38163
+     PhraseModel_4 = -0.58817
+     PhraseModel_5 = -0.0269
+     PhraseModel_6 = -0.27315
+       PassThrough = -0.16745
+        ---
+       1best avg score: 0.13169 (+0.13169)
+ 1best avg model score: 24.226 (+24.226)
+           avg # pairs: 1951.2
+        avg # rank err: 985.4
+     avg # margin viol: 951
+    non0 feature count: 12
+           avg list sz: 100
+           avg f count: 11.224
+(time 0.42 min, 5 s/S)
+
+Writing weights file to 'work/weights.1.1' ...
+done
+
+---
+Best iteration: 1 [SCORE 'stupid_bleu'=0.13169].
+This took 0.41667 min.
diff --git a/training/dtrain/examples/parallelized/work/shard.0.0.in b/training/dtrain/examples/parallelized/work/shard.0.0.in
new file mode 100644
index 00000000..92f9c78e
--- /dev/null
+++ b/training/dtrain/examples/parallelized/work/shard.0.0.in
@@ -0,0 +1,5 @@
+<seg grammar="grammar/grammar.out.0.gz" id="0">europas nach rassen geteiltes haus</seg>
+<seg grammar="grammar/grammar.out.1.gz" id="1">ein gemeinsames merkmal aller extremen rechten in europa ist ihr rassismus und die tatsache , daß sie das einwanderungsproblem als politischen hebel benutzen .</seg>
+<seg grammar="grammar/grammar.out.2.gz" id="2">der lega nord in italien , der vlaams block in den niederlanden , die anhänger von le pens nationaler front in frankreich , sind beispiele für parteien oder bewegungen , die sich um das gemeinsame thema : ablehnung der zuwanderung gebildet haben und um forderung nach einer vereinfachten politik , um sie zu regeln .</seg>
+<seg grammar="grammar/grammar.out.3.gz" id="3">während individuen wie jörg haidar und jean @-@ marie le pen kommen und ( leider nicht zu bald ) wieder gehen mögen , wird die rassenfrage aus der europäischer politik nicht so bald verschwinden .</seg>
+<seg grammar="grammar/grammar.out.4.gz" id="4">eine alternde einheimische bevölkerung und immer offenere grenzen vermehren die rassistische zersplitterung in den europäischen ländern .</seg>
diff --git a/training/dtrain/examples/parallelized/work/shard.0.0.refs b/training/dtrain/examples/parallelized/work/shard.0.0.refs
new file mode 100644
index 00000000..bef68fee
--- /dev/null
+++ b/training/dtrain/examples/parallelized/work/shard.0.0.refs
@@ -0,0 +1,5 @@
+europe 's divided racial house
+a common feature of europe 's extreme right is its racism and use of the immigration issue as a political wedge .
+the lega nord in italy , the vlaams blok in the netherlands , the supporters of le pen 's national front in france , are all examples of parties or movements formed on the common theme of aversion to immigrants and promotion of simplistic policies to control them .
+while individuals like jorg haidar and jean @-@ marie le pen may come and ( never to soon ) go , the race question will not disappear from european politics anytime soon .
+an aging population at home and ever more open borders imply increasing racial fragmentation in european countries .
diff --git a/training/dtrain/examples/parallelized/work/shard.1.0.in b/training/dtrain/examples/parallelized/work/shard.1.0.in
new file mode 100644
index 00000000..b7695ce7
--- /dev/null
+++ b/training/dtrain/examples/parallelized/work/shard.1.0.in
@@ -0,0 +1,5 @@
+<seg grammar="grammar/grammar.out.5.gz" id="5">die großen parteien der rechten und der linken mitte haben sich dem problem gestellt , in dem sie den kopf in den sand gesteckt und allen aussichten zuwider gehofft haben , es möge bald verschwinden .</seg>
+<seg grammar="grammar/grammar.out.6.gz" id="6">das aber wird es nicht , wie die geschichte des rassismus in amerika deutlich zeigt .</seg>
+<seg grammar="grammar/grammar.out.7.gz" id="7">die beziehungen zwischen den rassen standen in den usa über jahrzehnte - und tun das noch heute - im zentrum der politischen debatte . das ging so weit , daß rassentrennung genauso wichtig wie das einkommen wurde , - wenn nicht sogar noch wichtiger - um politische zuneigungen und einstellungen zu bestimmen .</seg>
+<seg grammar="grammar/grammar.out.8.gz" id="8">der erste schritt , um mit der rassenfrage umzugehen ist , ursache und folgen rassistischer feindseligkeiten zu verstehen , auch dann , wenn das bedeutet , unangenehme tatsachen aufzudecken .</seg>
+<seg grammar="grammar/grammar.out.9.gz" id="9">genau das haben in den usa eine große anzahl an forschungsvorhaben in wirtschaft , soziologie , psychologie und politikwissenschaft geleistet . diese forschungen zeigten , daß menschen unterschiedlicher rasse einander deutlich weniger vertrauen .</seg>
diff --git a/training/dtrain/examples/parallelized/work/shard.1.0.refs b/training/dtrain/examples/parallelized/work/shard.1.0.refs
new file mode 100644
index 00000000..6076f6d5
--- /dev/null
+++ b/training/dtrain/examples/parallelized/work/shard.1.0.refs
@@ -0,0 +1,5 @@
+mainstream parties of the center left and center right have confronted this prospect by hiding their heads in the ground , hoping against hope that the problem will disappear .
+it will not , as america 's racial history clearly shows .
+race relations in the us have been for decades - and remain - at the center of political debate , to the point that racial cleavages are as important as income , if not more , as determinants of political preferences and attitudes .
+the first step to address racial politics is to understand the origin and consequences of racial animosity , even if it means uncovering unpleasant truths .
+this is precisely what a large amount of research in economics , sociology , psychology and political science has done for the us .
diff --git a/training/dtrain/examples/parallelized/work/weights.0 b/training/dtrain/examples/parallelized/work/weights.0
new file mode 100644
index 00000000..ddd595a8
--- /dev/null
+++ b/training/dtrain/examples/parallelized/work/weights.0
@@ -0,0 +1,12 @@
+LanguageModel	0.7004298992212881
+PhraseModel_2	0.5576194336478857
+PhraseModel_1	0.41787318415343155
+PhraseModel_4	-0.46728502545635164
+PhraseModel_3	-0.029839521598455515
+Glue	-0.05760000000000068
+PhraseModel_6	-0.2716499999999978
+PhraseModel_0	-0.20831031065605327
+LanguageModel_OOV	-0.15205000000000077
+PassThrough	-0.1846500000000006
+WordPenalty	0.09636994553433414
+PhraseModel_5	-0.026900000000000257
diff --git a/training/dtrain/examples/parallelized/work/weights.0.0 b/training/dtrain/examples/parallelized/work/weights.0.0
new file mode 100644
index 00000000..c9370b18
--- /dev/null
+++ b/training/dtrain/examples/parallelized/work/weights.0.0
@@ -0,0 +1,12 @@
+WordPenalty	-0.0079041595706392243
+LanguageModel	0.44781580828279532
+LanguageModel_OOV	-0.04010000000000042
+Glue	0.26629999999999948
+PhraseModel_0	-0.19299677809125185
+PhraseModel_1	0.71321026861732773
+PhraseModel_2	0.85195540993310537
+PhraseModel_3	-0.43986310822842656
+PhraseModel_4	-0.44802855630415955
+PhraseModel_5	-0.053800000000000514
+PhraseModel_6	-0.17879999999999835
+PassThrough	-0.14770000000000036
diff --git a/training/dtrain/examples/parallelized/work/weights.0.1 b/training/dtrain/examples/parallelized/work/weights.0.1
new file mode 100644
index 00000000..8fad3de8
--- /dev/null
+++ b/training/dtrain/examples/parallelized/work/weights.0.1
@@ -0,0 +1,12 @@
+WordPenalty	0.080605055841244472
+LanguageModel	-0.026571720531022844
+LanguageModel_OOV	-0.30024999999999141
+Glue	-0.26989999999999842
+PhraseModel_2	0.92000295209089566
+PhraseModel_1	0.67450748692470841
+PhraseModel_4	-0.5920000014976784
+PhraseModel_3	-0.36402437203127397
+PhraseModel_6	-0.28754999999999603
+PhraseModel_0	-0.32076244202907672
+PassThrough	-0.33284999999999004
+PhraseModel_5	-0.026900000000000257
diff --git a/training/dtrain/examples/parallelized/work/weights.1 b/training/dtrain/examples/parallelized/work/weights.1
new file mode 100644
index 00000000..03058a16
--- /dev/null
+++ b/training/dtrain/examples/parallelized/work/weights.1
@@ -0,0 +1,12 @@
+PhraseModel_2	0.8365578543552836
+PhraseModel_4	-0.5900840266009169
+PhraseModel_1	0.5312000609786991
+PhraseModel_0	-0.3872342271319619
+PhraseModel_3	-0.3728279676912084
+Glue	-0.2938500000000036
+PhraseModel_6	-0.2803499999999967
+PassThrough	-0.25014999999999626
+LanguageModel_OOV	-0.21754999999999702
+LanguageModel	0.07306061161169894
+WordPenalty	0.09576193325966899
+PhraseModel_5	-0.026900000000000257
diff --git a/training/dtrain/examples/parallelized/work/weights.1.0 b/training/dtrain/examples/parallelized/work/weights.1.0
new file mode 100644
index 00000000..6a6a65c1
--- /dev/null
+++ b/training/dtrain/examples/parallelized/work/weights.1.0
@@ -0,0 +1,11 @@
+WordPenalty	0.20064405063930751
+LanguageModel	0.9530439901597807
+LanguageModel_OOV	-0.26400000000000112
+Glue	-0.38150000000000084
+PhraseModel_0	-0.22362384322085468
+PhraseModel_1	0.12253609968953538
+PhraseModel_2	0.26328345736266612
+PhraseModel_3	0.38018406503151553
+PhraseModel_4	-0.48654149460854373
+PhraseModel_6	-0.36449999999999722
+PassThrough	-0.22160000000000085
diff --git a/training/dtrain/examples/parallelized/work/weights.1.1 b/training/dtrain/examples/parallelized/work/weights.1.1
new file mode 100644
index 00000000..f56ea4a2
--- /dev/null
+++ b/training/dtrain/examples/parallelized/work/weights.1.1
@@ -0,0 +1,12 @@
+WordPenalty	0.1109188106780935
+LanguageModel	0.17269294375442074
+LanguageModel_OOV	-0.13485000000000266
+Glue	-0.3178000000000088
+PhraseModel_2	0.75311275661967159
+PhraseModel_1	0.38789263503268989
+PhraseModel_4	-0.58816805170415531
+PhraseModel_3	-0.38163156335114284
+PhraseModel_6	-0.27314999999999739
+PhraseModel_0	-0.45370601223484697
+PassThrough	-0.16745000000000249
+PhraseModel_5	-0.026900000000000257