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-rw-r--r--dtrain/test/example/cdec.ini3
-rw-r--r--dtrain/test/example/dtrain.ini2
-rw-r--r--dtrain/test/example/expected-output148
3 files changed, 72 insertions, 81 deletions
diff --git a/dtrain/test/example/cdec.ini b/dtrain/test/example/cdec.ini
index 6642107f..d5955f0e 100644
--- a/dtrain/test/example/cdec.ini
+++ b/dtrain/test/example/cdec.ini
@@ -17,7 +17,8 @@ feature_function=KLanguageModel test/example/nc-wmt11.en.srilm.gz
#feature_function=NonLatinCount
#feature_function=OutputIndicator
feature_function=RuleIdentityFeatures
-feature_function=RuleNgramFeatures
+feature_function=RuleSourceBigramFeatures
+feature_function=RuleTargetBigramFeatures
feature_function=RuleShape
#feature_function=SourceSpanSizeFeatures
#feature_function=SourceWordPenalty
diff --git a/dtrain/test/example/dtrain.ini b/dtrain/test/example/dtrain.ini
index c8ac7c3f..8338b2d3 100644
--- a/dtrain/test/example/dtrain.ini
+++ b/dtrain/test/example/dtrain.ini
@@ -5,7 +5,7 @@ decoder_config=test/example/cdec.ini # config for cdec
# weights for these features will be printed on each iteration
print_weights=Glue WordPenalty LanguageModel LanguageModel_OOV PhraseModel_0 PhraseModel_1 PhraseModel_2 PhraseModel_3 PhraseModel_4 PhraseModel_5 PhraseModel_6 PassThrough
tmp=/tmp
-stop_after=10 # stop epoch after 10 inputs
+stop_after=100 # stop epoch after 10 inputs
# interesting stuff
epochs=3 # run over input 3 times
diff --git a/dtrain/test/example/expected-output b/dtrain/test/example/expected-output
index 25d2c069..43798484 100644
--- a/dtrain/test/example/expected-output
+++ b/dtrain/test/example/expected-output
@@ -1,21 +1,10 @@
cdec cfg 'test/example/cdec.ini'
-feature: WordPenalty (no config parameters)
-State is 0 bytes for feature WordPenalty
-feature: KLanguageModel (with config parameters 'test/example/nc-wmt11.en.srilm.gz')
Loading the LM will be faster if you build a binary file.
Reading test/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
****************************************************************************************************
-Loaded 5-gram KLM from test/example/nc-wmt11.en.srilm.gz (MapSize=49581)
-State is 98 bytes for feature KLanguageModel test/example/nc-wmt11.en.srilm.gz
-feature: RuleIdentityFeatures (no config parameters)
-State is 0 bytes for feature RuleIdentityFeatures
-feature: RuleNgramFeatures (no config parameters)
-State is 0 bytes for feature RuleNgramFeatures
-feature: RuleShape (no config parameters)
Example feature: Shape_S00000_T00000
-State is 0 bytes for feature RuleShape
-Seeding random number sequence to 1072059181
+Seeding random number sequence to 2108658507
dtrain
Parameters:
@@ -33,93 +22,94 @@ Parameters:
pair threshold 0
select weights 'VOID'
l1 reg 0 'none'
+ max pairs 4294967295
cdec cfg 'test/example/cdec.ini'
input 'test/example/nc-wmt11.1k.gz'
output '-'
- stop_after 10
+ stop_after 100
(a dot represents 10 inputs)
Iteration #1 of 3.
- . 10
-Stopping after 10 input sentences.
+ .......... 100
+Stopping after 100 input sentences.
WEIGHTS
- Glue = -0.0293
- WordPenalty = +0.049075
- LanguageModel = +0.24345
- LanguageModel_OOV = -0.2029
- PhraseModel_0 = +0.0084102
- PhraseModel_1 = +0.021729
- PhraseModel_2 = +0.014922
- PhraseModel_3 = +0.104
- PhraseModel_4 = -0.14308
- PhraseModel_5 = +0.0247
- PhraseModel_6 = -0.012
- PassThrough = -0.2161
+ Glue = -0.236
+ WordPenalty = +0.056111
+ LanguageModel = +0.71011
+ LanguageModel_OOV = -0.489
+ PhraseModel_0 = -0.21332
+ PhraseModel_1 = -0.13038
+ PhraseModel_2 = +0.085148
+ PhraseModel_3 = -0.16982
+ PhraseModel_4 = -0.026332
+ PhraseModel_5 = +0.2133
+ PhraseModel_6 = +0.1002
+ PassThrough = -0.5541
---
- 1best avg score: 0.16872 (+0.16872)
- 1best avg model score: -1.8276 (-1.8276)
- avg # pairs: 1121.1
- avg # rank err: 555.6
+ 1best avg score: 0.16928 (+0.16928)
+ 1best avg model score: 2.4454 (+2.4454)
+ avg # pairs: 1616.2
+ avg # rank err: 769.6
avg # margin viol: 0
- non0 feature count: 277
- avg list sz: 77.2
- avg f count: 90.96
-(time 0.1 min, 0.6 s/S)
+ non0 feature count: 4068
+ avg list sz: 96.65
+ avg f count: 118.01
+(time 1.3 min, 0.79 s/S)
Iteration #2 of 3.
- . 10
+ .......... 100
WEIGHTS
- Glue = -0.3526
- WordPenalty = +0.067576
- LanguageModel = +1.155
- LanguageModel_OOV = -0.2728
- PhraseModel_0 = -0.025529
- PhraseModel_1 = +0.095869
- PhraseModel_2 = +0.094567
- PhraseModel_3 = +0.12482
- PhraseModel_4 = -0.36533
- PhraseModel_5 = +0.1068
- PhraseModel_6 = -0.1517
- PassThrough = -0.286
+ Glue = -0.1721
+ WordPenalty = -0.14132
+ LanguageModel = +0.56023
+ LanguageModel_OOV = -0.6786
+ PhraseModel_0 = +0.14155
+ PhraseModel_1 = +0.34218
+ PhraseModel_2 = +0.22954
+ PhraseModel_3 = -0.24762
+ PhraseModel_4 = -0.25848
+ PhraseModel_5 = -0.0453
+ PhraseModel_6 = -0.0264
+ PassThrough = -0.7436
---
- 1best avg score: 0.18394 (+0.015221)
- 1best avg model score: 3.205 (+5.0326)
- avg # pairs: 1168.3
- avg # rank err: 594.8
+ 1best avg score: 0.19585 (+0.02657)
+ 1best avg model score: -16.311 (-18.757)
+ avg # pairs: 1475.8
+ avg # rank err: 668.48
avg # margin viol: 0
- non0 feature count: 543
- avg list sz: 77.5
- avg f count: 85.916
-(time 0.083 min, 0.5 s/S)
+ non0 feature count: 6300
+ avg list sz: 96.08
+ avg f count: 114.92
+(time 1.3 min, 0.76 s/S)
Iteration #3 of 3.
- . 10
+ .......... 100
WEIGHTS
- Glue = -0.392
- WordPenalty = +0.071963
- LanguageModel = +0.81266
- LanguageModel_OOV = -0.4177
- PhraseModel_0 = -0.2649
- PhraseModel_1 = -0.17931
- PhraseModel_2 = +0.038261
- PhraseModel_3 = +0.20261
- PhraseModel_4 = -0.42621
- PhraseModel_5 = +0.3198
- PhraseModel_6 = -0.1437
- PassThrough = -0.4309
+ Glue = -0.1577
+ WordPenalty = -0.086902
+ LanguageModel = +0.30136
+ LanguageModel_OOV = -0.7848
+ PhraseModel_0 = +0.11743
+ PhraseModel_1 = +0.11142
+ PhraseModel_2 = -0.0053865
+ PhraseModel_3 = -0.18731
+ PhraseModel_4 = -0.67144
+ PhraseModel_5 = +0.1236
+ PhraseModel_6 = -0.2665
+ PassThrough = -0.8498
---
- 1best avg score: 0.2962 (+0.11225)
- 1best avg model score: -36.274 (-39.479)
- avg # pairs: 1109.6
- avg # rank err: 515.9
+ 1best avg score: 0.20034 (+0.0044978)
+ 1best avg model score: -7.2775 (+9.0336)
+ avg # pairs: 1578.6
+ avg # rank err: 705.77
avg # margin viol: 0
- non0 feature count: 741
- avg list sz: 77
- avg f count: 88.982
-(time 0.083 min, 0.5 s/S)
+ non0 feature count: 7313
+ avg list sz: 96.84
+ avg f count: 124.48
+(time 1.5 min, 0.9 s/S)
Writing weights file to '-' ...
done
---
-Best iteration: 3 [SCORE 'stupid_bleu'=0.2962].
-This took 0.26667 min.
+Best iteration: 3 [SCORE 'stupid_bleu'=0.20034].
+This took 4.0833 min.