From bb9309432fd35e95cf88b630853a928a3e3228c3 Mon Sep 17 00:00:00 2001 From: Patrick Simianer Date: Wed, 1 Aug 2012 18:27:51 +0200 Subject: Makefile.am, dtrain output, python build --- dtrain/test/example/cdec.ini | 3 +- dtrain/test/example/dtrain.ini | 2 +- dtrain/test/example/expected-output | 148 +++++++++++++++++------------------- 3 files changed, 72 insertions(+), 81 deletions(-) (limited to 'dtrain') 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. -- cgit v1.2.3