From b6d085d66decb602e88f32fb5cef27b10e83e69e 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/expected-output | 148 +++++++++++++++++------------------- 1 file changed, 69 insertions(+), 79 deletions(-) (limited to 'dtrain/test/example/expected-output') 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 From 8367ba7f165ce2ea43e3f2853d573133d58898fd Mon Sep 17 00:00:00 2001 From: Patrick Simianer Date: Mon, 5 Nov 2012 18:57:39 +0100 Subject: build fix, default learning rate --- decoder/ff_rules.h | 1 + dtrain/dtrain.cc | 4 +- dtrain/dtrain.h | 3 +- dtrain/test/example/dtrain.ini | 8 +-- dtrain/test/example/expected-output | 128 ++++++++++++++---------------------- 5 files changed, 59 insertions(+), 85 deletions(-) (limited to 'dtrain/test/example/expected-output') diff --git a/decoder/ff_rules.h b/decoder/ff_rules.h index dc9a15d5..b100ec34 100644 --- a/decoder/ff_rules.h +++ b/decoder/ff_rules.h @@ -5,6 +5,7 @@ #include #include "trule.h" #include "ff.h" +#include "hg.h" #include "array2d.h" #include "wordid.h" 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()->default_value(0.), "bleu [0,1] threshold to filter pairs") ("N", po::value()->default_value(4), "N for Ngrams (BLEU)") ("scorer", po::value()->default_value("stupid_bleu"), "scoring: bleu, stupid_, smooth_, approx_, lc_") - ("learning_rate", po::value()->default_value(0.0001), "learning rate") + ("learning_rate", po::value()->default_value(1.0), "learning rate") ("gamma", po::value()->default_value(0.), "gamma for SVM (0 for perceptron)") ("select_weights", po::value()->default_value("last"), "output best, last, avg weights ('VOID' to throw away)") ("rescale", po::value()->zero_tokens(), "rescale weight vector after each input") ("l1_reg", po::value()->default_value("none"), "apply l1 regularization as in 'Tsuroka et al' (2010)") ("l1_reg_strength", po::value(), "l1 regularization strength") - ("fselect", po::value()->default_value(-1), "select top x percent (or by threshold) of features after each epoch NOT IMPL") // TODO + ("fselect", po::value()->default_value(-1), "select top x percent (or by threshold) of features after each epoch NOT IMPLEMENTED") // TODO ("approx_bleu_d", po::value()->default_value(0.9), "discount for approx. BLEU") ("scale_bleu_diff", po::value()->zero_tokens(), "learning rate <- bleu diff of a misranked pair") ("loss_margin", po::value()->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. -- cgit v1.2.3