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-rw-r--r--dtrain/dtrain.cc4
-rw-r--r--dtrain/dtrain.h3
-rw-r--r--dtrain/test/example/dtrain.ini8
-rw-r--r--dtrain/test/example/expected-output128
4 files changed, 58 insertions, 85 deletions
diff --git a/dtrain/dtrain.cc b/dtrain/dtrain.cc
index b7a4bb6f..18286668 100644
--- a/dtrain/dtrain.cc
+++ b/dtrain/dtrain.cc
@@ -24,13 +24,13 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg)
("pair_threshold", po::value<score_t>()->default_value(0.), "bleu [0,1] threshold to filter pairs")
("N", po::value<unsigned>()->default_value(4), "N for Ngrams (BLEU)")
("scorer", po::value<string>()->default_value("stupid_bleu"), "scoring: bleu, stupid_, smooth_, approx_, lc_")
- ("learning_rate", po::value<weight_t>()->default_value(0.0001), "learning rate")
+ ("learning_rate", po::value<weight_t>()->default_value(1.0), "learning rate")
("gamma", po::value<weight_t>()->default_value(0.), "gamma for SVM (0 for perceptron)")
("select_weights", po::value<string>()->default_value("last"), "output best, last, avg weights ('VOID' to throw away)")
("rescale", po::value<bool>()->zero_tokens(), "rescale weight vector after each input")
("l1_reg", po::value<string>()->default_value("none"), "apply l1 regularization as in 'Tsuroka et al' (2010)")
("l1_reg_strength", po::value<weight_t>(), "l1 regularization strength")
- ("fselect", po::value<weight_t>()->default_value(-1), "select top x percent (or by threshold) of features after each epoch NOT IMPL") // TODO
+ ("fselect", po::value<weight_t>()->default_value(-1), "select top x percent (or by threshold) of features after each epoch NOT IMPLEMENTED") // TODO
("approx_bleu_d", po::value<score_t>()->default_value(0.9), "discount for approx. BLEU")
("scale_bleu_diff", po::value<bool>()->zero_tokens(), "learning rate <- bleu diff of a misranked pair")
("loss_margin", po::value<weight_t>()->default_value(0.), "update if no error in pref pair but model scores this near")
diff --git a/dtrain/dtrain.h b/dtrain/dtrain.h
index 7e084a79..4b6f415c 100644
--- a/dtrain/dtrain.h
+++ b/dtrain/dtrain.h
@@ -3,7 +3,7 @@
#undef DTRAIN_FASTER_PERCEPTRON // only look at misranked pairs
// DO NOT USE WITH SVM!
-#define DTRAIN_LOCAL
+//#define DTRAIN_LOCAL
#define DTRAIN_DOTS 10 // after how many inputs to display a '.'
#define DTRAIN_GRAMMAR_DELIM "########EOS########"
#define DTRAIN_SCALE 100000
@@ -22,7 +22,6 @@
#include "filelib.h"
-
using namespace std;
using namespace dtrain;
namespace po = boost::program_options;
diff --git a/dtrain/test/example/dtrain.ini b/dtrain/test/example/dtrain.ini
index 8338b2d3..72d50ca1 100644
--- a/dtrain/test/example/dtrain.ini
+++ b/dtrain/test/example/dtrain.ini
@@ -1,18 +1,18 @@
input=test/example/nc-wmt11.1k.gz # use '-' for STDIN
output=- # a weights file (add .gz for gzip compression) or STDOUT '-'
-select_weights=VOID # don't output weights
+select_weights=VOID # don't output weights
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=100 # stop epoch after 10 inputs
+stop_after=10 # stop epoch after 10 inputs
# interesting stuff
-epochs=3 # run over input 3 times
+epochs=2 # run over input 2 times
k=100 # use 100best lists
N=4 # optimize (approx) BLEU4
scorer=stupid_bleu # use 'stupid' BLEU+1
-learning_rate=0.0001 # learning rate
+learning_rate=1.0 # learning rate, don't care if gamma=0 (perceptron)
gamma=0 # use SVM reg
sample_from=kbest # use kbest lists (as opposed to forest)
filter=uniq # only unique entries in kbest (surface form)
diff --git a/dtrain/test/example/expected-output b/dtrain/test/example/expected-output
index 43798484..05326763 100644
--- a/dtrain/test/example/expected-output
+++ b/dtrain/test/example/expected-output
@@ -4,17 +4,17 @@ 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
****************************************************************************************************
Example feature: Shape_S00000_T00000
-Seeding random number sequence to 2108658507
+Seeding random number sequence to 2912000813
dtrain
Parameters:
k 100
N 4
- T 3
+ T 2
scorer 'stupid_bleu'
sample from 'kbest'
filter 'uniq'
- learning rate 0.0001
+ learning rate 1
gamma 0
loss margin 0
pairs 'XYX'
@@ -26,90 +26,64 @@ Parameters:
cdec cfg 'test/example/cdec.ini'
input 'test/example/nc-wmt11.1k.gz'
output '-'
- stop_after 100
+ stop_after 10
(a dot represents 10 inputs)
-Iteration #1 of 3.
- .......... 100
-Stopping after 100 input sentences.
+Iteration #1 of 2.
+ . 10
+Stopping after 10 input sentences.
WEIGHTS
- 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
+ Glue = -637
+ WordPenalty = +1064
+ LanguageModel = +1175.3
+ LanguageModel_OOV = -1437
+ PhraseModel_0 = +1935.6
+ PhraseModel_1 = +2499.3
+ PhraseModel_2 = +964.96
+ PhraseModel_3 = +1410.8
+ PhraseModel_4 = -5977.9
+ PhraseModel_5 = +522
+ PhraseModel_6 = +1089
+ PassThrough = -1308
---
- 1best avg score: 0.16928 (+0.16928)
- 1best avg model score: 2.4454 (+2.4454)
- avg # pairs: 1616.2
- avg # rank err: 769.6
+ 1best avg score: 0.16963 (+0.16963)
+ 1best avg model score: 64485 (+64485)
+ avg # pairs: 1494.4
+ avg # rank err: 702.6
avg # margin viol: 0
- non0 feature count: 4068
- avg list sz: 96.65
- avg f count: 118.01
-(time 1.3 min, 0.79 s/S)
+ non0 feature count: 528
+ avg list sz: 85.7
+ avg f count: 102.75
+(time 0.083 min, 0.5 s/S)
-Iteration #2 of 3.
- .......... 100
+Iteration #2 of 2.
+ . 10
WEIGHTS
- 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
+ Glue = -1196
+ WordPenalty = +809.52
+ LanguageModel = +3112.1
+ LanguageModel_OOV = -1464
+ PhraseModel_0 = +3895.5
+ PhraseModel_1 = +4683.4
+ PhraseModel_2 = +1092.8
+ PhraseModel_3 = +1079.6
+ PhraseModel_4 = -6827.7
+ PhraseModel_5 = -888
+ PhraseModel_6 = +142
+ PassThrough = -1335
---
- 1best avg score: 0.19585 (+0.02657)
- 1best avg model score: -16.311 (-18.757)
- avg # pairs: 1475.8
- avg # rank err: 668.48
+ 1best avg score: 0.277 (+0.10736)
+ 1best avg model score: -3110.5 (-67595)
+ avg # pairs: 1144.2
+ avg # rank err: 529.1
avg # margin viol: 0
- 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.
- .......... 100
-WEIGHTS
- 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: 0.20034 (+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: 7313
- avg list sz: 96.84
- avg f count: 124.48
-(time 1.5 min, 0.9 s/S)
+ non0 feature count: 859
+ avg list sz: 74.9
+ avg f count: 112.84
+(time 0.067 min, 0.4 s/S)
Writing weights file to '-' ...
done
---
-Best iteration: 3 [SCORE 'stupid_bleu'=0.20034].
-This took 4.0833 min.
+Best iteration: 2 [SCORE 'stupid_bleu'=0.277].
+This took 0.15 min.
generated by cgit v1.2.3 (git 2.46.0) at 2025-10-31 00:38:47 +0000