From 93cca9f82039b7fc375d81e92e4389c0658c0441 Mon Sep 17 00:00:00 2001 From: Hal Daume III Date: Sun, 21 Sep 2014 11:30:36 -0400 Subject: integrated new version of search, updated relevant tests (and removed ones that use beam, since beam is still not supported) --- test/RunTests | 105 +++++++-------------- test/train-sets/ref/argmax_data.stderr | 20 ++-- test/train-sets/ref/searn_small.stderr | 0 test/train-sets/ref/searn_small.stdout | 0 test/train-sets/ref/searn_wsj.stderr | 0 test/train-sets/ref/searn_wsj.stdout | 0 test/train-sets/ref/searn_wsj2.dat.stderr | 0 test/train-sets/ref/searn_wsj2.dat.stdout | 0 test/train-sets/ref/sequence_data.ldf.test.stderr | 8 +- test/train-sets/ref/sequence_data.ldf.train.stderr | 22 ++--- .../ref/sequence_data.nonldf.test.stderr | 8 +- .../ref/sequence_data.nonldf.train.stderr | 16 ++-- .../ref/sequencespan_data.nonldf-bilou.test.stderr | 8 +- .../sequencespan_data.nonldf-bilou.train.stderr | 18 ++-- .../ref/sequencespan_data.nonldf.test.stderr | 8 +- .../ref/sequencespan_data.nonldf.train.stderr | 18 ++-- 16 files changed, 89 insertions(+), 142 deletions(-) delete mode 100644 test/train-sets/ref/searn_small.stderr delete mode 100644 test/train-sets/ref/searn_small.stdout delete mode 100644 test/train-sets/ref/searn_wsj.stderr delete mode 100644 test/train-sets/ref/searn_wsj.stdout delete mode 100644 test/train-sets/ref/searn_wsj2.dat.stderr delete mode 100644 test/train-sets/ref/searn_wsj2.dat.stdout (limited to 'test') diff --git a/test/RunTests b/test/RunTests index 20ea2de8..048c4815 100755 --- a/test/RunTests +++ b/test/RunTests @@ -798,14 +798,14 @@ __DATA__ {VW} -k --ect 10 --error 3 -c --passes 10 --invariant train-sets/multiclass --holdout_off train-sets/ref/multiclass.stderr -# Test 13: Run searn on wsj_small for 12 passes, 4 passes per policy, extra features -{VW} -k -c -d train-sets/wsj_small.dat.gz --passes 12 --invariant --search_passes_per_policy 4 --search_task sequence --search 5 --search_history 2 --search_bigrams --search_features 1 --quiet --holdout_off - train-sets/ref/searn_wsj.stderr +# Test 13: Run search (dagger) on wsj_small for 6 passes extra features +{VW} -k -c -d train-sets/wsj_small.dat.gz --passes 6 --search_task sequence --search 45 --search_alpha 1e-6 --search_max_bias_ngram_length 2 --search_max_quad_ngram_length 1 --holdout_off + train-sets/ref/search_wsj.stderr -# Test 14: Run searn (wap) on wsj_small for 2 passes, 1 pass per policy, extra features -{VW} -k -b 19 -c -d train-sets/wsj_small.dat.gz --passes 2 --invariant --search_passes_per_policy 1 --search_task sequence --search 5 --wap 5 --search_history 2 --search_bigrams --search_features 1 --quiet --holdout_off - train-sets/ref/searn_wsj2.dat.stdout - train-sets/ref/searn_wsj2.dat.stderr +# Test 14: Run search (searn) on wsj_small for 6 passes extra features +{VW} -k -c -d train-sets/wsj_small.dat.gz --passes 6 --search_task sequence --search 45 --search_alpha 1e-6 --search_max_bias_ngram_length 2 --search_max_quad_ngram_length 1 --holdout_off --search_passes_per_policy 3 --search_interpolation policy + train-sets/ref/search_wsj2.dat.stdout + train-sets/ref/search_wsj2.dat.stderr # Test 15: LBFGS on zero derivative input {VW} -k -c -d train-sets/zero.dat --loss_function=squared -b 20 --bfgs --mem 7 --passes 5 --l2 1.0 --holdout_off @@ -821,9 +821,9 @@ __DATA__ {LDA} -k --lda 100 --lda_alpha 0.01 --lda_rho 0.01 --lda_D 1000 -l 1 -b 13 --minibatch 128 --invariant train-sets/wiki1K.dat train-sets/ref/wiki1K.stderr -# Test 18: Run searn on seq_small for 12 passes, 4 passes per policy -{VW} -k -c -d train-sets/seq_small --passes 12 --invariant --search_passes_per_policy 4 --search 4 --search_task sequence --quiet --holdout_off - train-sets/ref/searn_small.stderr +# Test 18: Run search on seq_small for 12 passes, 4 passes per policy +{VW} -k -c -d train-sets/seq_small --passes 12 --invariant --search 4 --search_task sequence --holdout_off + train-sets/ref/search_small.stderr # Test 19: neural network 3-parity with 2 hidden units {VW} -k -c -d train-sets/3parity --hash all --passes 3000 -b 16 --nn 2 -l 10 --invariant -f models/0021.model --random_seed 15 --quiet --holdout_off @@ -932,141 +932,106 @@ __DATA__ {VW} -k -d train-sets/lda-2pass-hang.dat --lda 10 -c --passes 2 --holdout_off train-sets/ref/lda-2pass-hang.stderr -# Test 43: searn sequence labeling, non-ldf train +# Test 43: search sequence labeling, non-ldf train {VW} -k -c -d train-sets/sequence_data --passes 20 --invariant --search_rollout oracle --search_alpha 1e-8 --search_task sequence --search 5 --holdout_off -f models/sequence_data.model train-sets/ref/sequence_data.nonldf.train.stderr -# Test 44: searn sequence labeling, non-ldf test +# Test 44: search sequence labeling, non-ldf test {VW} -d train-sets/sequence_data -t -i models/sequence_data.model -p sequence_data.predict train-sets/ref/sequence_data.nonldf.test.stderr train-sets/ref/sequence_data.nonldf.test.predict -# Test 45: searn sequence labeling, non-ldf test, beam 1 -{VW} -d train-sets/sequence_data -t -i models/sequence_data.model -p sequence_data.predict --search_beam 1 - train-sets/ref/sequence_data.nonldf.test-beam1.stderr - train-sets/ref/sequence_data.nonldf.test-beam1.predict +# Test 45: make sure that history works +{VW} -k -c -d train-sets/seq_small2 --passes 4 --search 4 --search_task sequence --holdout_off + train-sets/ref/search_small2.stderr -# Test 46: searn sequence labeling, non-ldf test, beam 20 -{VW} -d train-sets/sequence_data -t -i models/sequence_data.model -p sequence_data.predict --search_beam 20 --search_kbest 20 - train-sets/ref/sequence_data.nonldf.test-beam20.stderr - train-sets/ref/sequence_data.nonldf.test-beam20.predict - -# Test 47: searn sequence labeling, ldf train -{VW} -k -c -d train-sets/sequence_data --passes 20 --invariant --search_rollout oracle --search_alpha 1e-8 --search_task sequence_demoldf --csoaa_ldf m --search 5 --holdout_off -f models/sequence_data.model +# Test 46: search sequence labeling, ldf train +{VW} -k -c -d train-sets/sequence_data --passes 20 --search_rollout oracle --search_alpha 1e-8 --search_task sequence_demoldf --csoaa_ldf m --search 5 --holdout_off -f models/sequence_data.model train-sets/ref/sequence_data.ldf.train.stderr -# Test 48: searn sequence labeling, ldf test +# Test 47: search sequence labeling, ldf test {VW} -d train-sets/sequence_data -t -i models/sequence_data.model -p sequence_data.predict train-sets/ref/sequence_data.ldf.test.stderr train-sets/ref/sequence_data.ldf.test.predict -# Test 49: searn sequence labeling, ldf test, beam 1 -{VW} -d train-sets/sequence_data -t -i models/sequence_data.model -p sequence_data.predict --search_beam 1 - train-sets/ref/sequence_data.ldf.test-beam1.stderr - train-sets/ref/sequence_data.ldf.test-beam1.predict - -# Test 50: searn sequence labeling, ldf test, beam 20 -{VW} -d train-sets/sequence_data -t -i models/sequence_data.model -p sequence_data.predict --search_beam 20 --search_kbest 20 - train-sets/ref/sequence_data.ldf.test-beam20.stderr - train-sets/ref/sequence_data.ldf.test-beam20.predict - -# Test 51: searn sequence SPAN labeling BIO, non-ldf train +# Test 48: search sequence SPAN labeling BIO, non-ldf train {VW} -k -c -d train-sets/sequencespan_data --passes 20 --invariant --search_rollout oracle --search_alpha 1e-8 --search_task sequencespan --search 7 --holdout_off -f models/sequencespan_data.model train-sets/ref/sequencespan_data.nonldf.train.stderr -# Test 52: searn sequence SPAN labeling BIO, non-ldf test +# Test 49: search sequence SPAN labeling BIO, non-ldf test {VW} -d train-sets/sequencespan_data -t -i models/sequencespan_data.model -p sequencespan_data.predict train-sets/ref/sequencespan_data.nonldf.test.stderr train-sets/ref/sequencespan_data.nonldf.test.predict -# Test 53: searn sequence SPAN labeling BIO, non-ldf test, beam 1 -{VW} -d train-sets/sequencespan_data -t -i models/sequencespan_data.model -p sequencespan_data.predict --search_beam 1 - train-sets/ref/sequencespan_data.nonldf.test-beam1.stderr - train-sets/ref/sequencespan_data.nonldf.test-beam1.predict - -# Test 54: searn sequence SPAN labeling BIO, non-ldf test, beam 20 -{VW} -d train-sets/sequencespan_data -t --search_span_bilou -i models/sequencespan_data.model --search_beam 20 --search_kbest 20 --quiet - train-sets/ref/sequencespan_data.nonldf.test-beam20.stderr - -# Test 55: searn sequence SPAN labeling BILOU, non-ldf train +# Test 50: search sequence SPAN labeling BILOU, non-ldf train {VW} -k -c -d train-sets/sequencespan_data --passes 20 --invariant --search_rollout oracle --search_alpha 1e-8 --search_task sequencespan --search_span_bilou --search 7 --holdout_off -f models/sequencespan_data.model train-sets/ref/sequencespan_data.nonldf-bilou.train.stderr -# Test 56: searn sequence SPAN labeling BILOU, non-ldf test +# Test 51: search sequence SPAN labeling BILOU, non-ldf test {VW} -d train-sets/sequencespan_data -t --search_span_bilou -i models/sequencespan_data.model -p sequencespan_data.predict train-sets/ref/sequencespan_data.nonldf-bilou.test.stderr train-sets/ref/sequencespan_data.nonldf-bilou.test.predict -# Test 57: searn sequence SPAN labeling BILOU, non-ldf test, beam 1 -{VW} -d train-sets/sequencespan_data -t --search_span_bilou -i models/sequencespan_data.model -p sequencespan_data.predict --search_beam 1 - train-sets/ref/sequencespan_data.nonldf-bilou.test-beam1.stderr - train-sets/ref/sequencespan_data.nonldf-bilou.test-beam1.predict - -# Test 58: searn sequence SPAN labeling BILOU, non-ldf test, beam 20 -{VW} -d train-sets/sequencespan_data -t --search_span_bilou -i models/sequencespan_data.model -p sequencespan_data.predict --search_beam 20 --search_kbest 20 - train-sets/ref/sequencespan_data.nonldf-bilou.test-beam20.stderr - train-sets/ref/sequencespan_data.nonldf-bilou.test-beam20.predict - -# Test 59: silly test for "argmax" task +# Test 52: silly test for "argmax" task {VW} -d train-sets/argmax_data -k -c --passes 20 --search_rollout oracle --search_alpha 1e-8 --search_task argmax --search 2 --holdout_off train-sets/ref/argmax_data.stderr -# Test 60: (holdout-broken regression) +# Test 53: (holdout-broken regression) # ensure we have no holdout loss of '0 h' {VW} -k -c --passes 2 train-sets/0001.dat train-sets/ref/holdout-loss-not-zero.stderr -# Test 61: stagewise poly with exponent 0.25 +# Test 54: stagewise poly with exponent 0.25 ####in the following stage_poly tests, there are minute differences in losses, which are not being fuzzy-diffed; ####thus the stderr is cleared (--quiet) and only comparing (fuzzy-diffed) predictions. {VW} --stage_poly --sched_exponent 0.25 --batch_sz 1000 --batch_sz_no_doubling -d train-sets/rcv1_small.dat -p stage_poly.s025.predict --quiet train-sets/ref/stage_poly.s025.stderr train-sets/ref/stage_poly.s025.predict -# Test 62: stagewise poly with exponent 1.0 +# Test 55: stagewise poly with exponent 1.0 {VW} --stage_poly --sched_exponent 1.0 --batch_sz 1000 --batch_sz_no_doubling -d train-sets/rcv1_small.dat --quiet train-sets/ref/stage_poly.s100.stderr -# Test 63: stagewise poly with exponent 0.25 and doubling batches +# Test 56: stagewise poly with exponent 0.25 and doubling batches {VW} --stage_poly --sched_exponent 0.25 --batch_sz 1000 -d train-sets/rcv1_small.dat -p stage_poly.s025.doubling.predict --quiet train-sets/ref/stage_poly.s025.doubling.stderr train-sets/ref/stage_poly.s025.doubling.predict -# Test 64: stagewise poly with exponent 1.0 and doubling batches +# Test 57: stagewise poly with exponent 1.0 and doubling batches {VW} --stage_poly --sched_exponent 1.0 --batch_sz 1000 -d train-sets/rcv1_small.dat -p stage_poly.s100.doubling.predict --quiet train-sets/ref/stage_poly.s100.doubling.stderr train-sets/ref/stage_poly.s100.doubling.predict -# Test 65: library test, train the initial model +# Test 58: library test, train the initial model {VW} -c -k -d train-sets/library_train -f models/library_train.w -q st --passes 100 --hash all --noconstant --csoaa_ldf m --holdout_off train-sets/ref/library_train.stdout train-sets/ref/library_train.stderr -# Test 66: library test, run ezexample_predict +# Test 59: library test, run ezexample_predict ../library/ezexample_predict models/library_train.w train-sets/ref/ezexample_predict.stdout train-sets/ref/ezexample_predict.stderr -# Test 67: empty test, bad builds (without make clean) +# Test 60: empty test, bad builds (without make clean) # sometimes cause a SEGV even on empty input {VW} /dev/null train-sets/ref/empty-set.stderr -# Test 68: daemon test +# Test 61: daemon test ./daemon-test.sh test-sets/ref/vw-daemon.stdout -# Test 69: SVM linear kernel +# Test 62: SVM linear kernel {VW} --ksvm --l2 1 --reprocess 5 -b 18 -p train-sets/ref/ksvm_train.linear.predict -d train-sets/rcv1_smaller.dat train-sets/ref/ksvm_train.linear.stderr train-sets/ref/ksvm_train.linear.predict -# Test 70: SVM polynomial kernel +# Test 63: SVM polynomial kernel {VW} --ksvm --l2 1 --reprocess 5 -b 18 --kernel poly -p train-sets/ref/ksvm_train.poly.predict -d train-sets/rcv1_smaller.dat train-sets/ref/ksvm_train.poly.stderr train-sets/ref/ksvm_train.poly.predict -# Test 71: SVM rbf kernel +# Test 64: SVM rbf kernel {VW} --ksvm --l2 1 --reprocess 5 -b 18 --kernel rbf -p train-sets/ref/ksvm_train.rbf.predict -d train-sets/rcv1_smaller.dat train-sets/ref/ksvm_train.rbf.stderr train-sets/ref/ksvm_train.rbf.predict diff --git a/test/train-sets/ref/argmax_data.stderr b/test/train-sets/ref/argmax_data.stderr index 6357dbac..84ca90df 100644 --- a/test/train-sets/ref/argmax_data.stderr +++ b/test/train-sets/ref/argmax_data.stderr @@ -6,17 +6,15 @@ decay_learning_rate = 1 creating cache_file = train-sets/argmax_data.cache Reading datafile = train-sets/argmax_data num sources = 1 -average since example example current current current -loss last counter weight label predict features -average since sequence example current label current predicted current cur cur predic. examples -loss last counter weight sequence prefix sequence prefix features pass pol made gener. -10.000000 10.000000 1 1.000000 [2 ] [1 ] 15 0 0 5 5 -5.500000 1.000000 2 2.000000 [1 ] [2 ] 12 0 0 9 9 -5.250000 5.000000 4 4.000000 [2 ] [1 ] 9 0 0 15 15 -2.875000 0.500000 8 8.000000 [2 ] [2 ] 9 1 0 30 30 -1.687500 0.500000 16 16.000000 [2 ] [2 ] 9 3 0 60 60 -1.093750 0.500000 32 32.000000 [2 ] [2 ] 9 7 0 120 120 -0.796875 0.500000 64 64.000000 [2 ] [2 ] 9 15 0 240 240 +average since instance current true current predicted cur cur predic cache examples +loss last counter output prefix output prefix pass pol made hits gener beta +10.000000 10.000000 1 [2 ] [1 ] 0 0 5 0 5 0.000000 +5.500000 1.000000 2 [1 ] [2 ] 0 0 9 0 9 0.000000 +5.250000 5.000000 4 [2 ] [1 ] 0 0 15 0 15 0.000000 +2.875000 0.500000 8 [2 ] [2 ] 1 0 30 0 30 0.000000 +1.687500 0.500000 16 [2 ] [2 ] 3 0 60 0 60 0.000001 +1.093750 0.500000 32 [2 ] [2 ] 7 0 120 0 120 0.000001 +0.796875 0.500000 64 [2 ] [2 ] 15 0 240 0 240 0.000002 finished run number of examples per pass = 4 diff --git a/test/train-sets/ref/searn_small.stderr b/test/train-sets/ref/searn_small.stderr deleted file mode 100644 index e69de29b..00000000 diff --git a/test/train-sets/ref/searn_small.stdout b/test/train-sets/ref/searn_small.stdout deleted file mode 100644 index e69de29b..00000000 diff --git a/test/train-sets/ref/searn_wsj.stderr b/test/train-sets/ref/searn_wsj.stderr deleted file mode 100644 index e69de29b..00000000 diff --git a/test/train-sets/ref/searn_wsj.stdout b/test/train-sets/ref/searn_wsj.stdout deleted file mode 100644 index e69de29b..00000000 diff --git a/test/train-sets/ref/searn_wsj2.dat.stderr b/test/train-sets/ref/searn_wsj2.dat.stderr deleted file mode 100644 index e69de29b..00000000 diff --git a/test/train-sets/ref/searn_wsj2.dat.stdout b/test/train-sets/ref/searn_wsj2.dat.stdout deleted file mode 100644 index e69de29b..00000000 diff --git a/test/train-sets/ref/sequence_data.ldf.test.stderr b/test/train-sets/ref/sequence_data.ldf.test.stderr index 3b66edd3..7deebd56 100644 --- a/test/train-sets/ref/sequence_data.ldf.test.stderr +++ b/test/train-sets/ref/sequence_data.ldf.test.stderr @@ -7,11 +7,9 @@ predictions = sequence_data.predict using no cache Reading datafile = train-sets/sequence_data num sources = 1 -average since example example current current current -loss last counter weight label predict features -average since sequence example current label current predicted current cur cur predic. examples -loss last counter weight sequence prefix sequence prefix features pass pol made gener. -0.000000 0.000000 1 1.000000 [5 4 3 2 1 ] [5 4 3 2 1 ] 50 0 0 25 0 +average since instance current true current predicted cur cur predic cache examples +loss last counter output prefix output prefix pass pol made hits gener beta +0.000000 0.000000 1 [5 4 3 2 1 ] [5 4 3 2 1 ] 0 0 5 0 0 0.000000 finished run number of examples per pass = 1 diff --git a/test/train-sets/ref/sequence_data.ldf.train.stderr b/test/train-sets/ref/sequence_data.ldf.train.stderr index 243e7c71..052ada4a 100644 --- a/test/train-sets/ref/sequence_data.ldf.train.stderr +++ b/test/train-sets/ref/sequence_data.ldf.train.stderr @@ -1,21 +1,19 @@ final_regressor = models/sequence_data.model Num weight bits = 18 -learning rate = 10 -initial_t = 1 +learning rate = 0.5 +initial_t = 0 power_t = 0.5 decay_learning_rate = 1 creating cache_file = train-sets/sequence_data.cache Reading datafile = train-sets/sequence_data num sources = 1 -average since example example current current current -loss last counter weight label predict features -average since sequence example current label current predicted current cur cur predic. examples -loss last counter weight sequence prefix sequence prefix features pass pol made gener. -4.000000 4.000000 1 1.000000 [5 4 3 2 1 ] [1 1 1 1 1 ] 50 0 0 25 5 -2.000000 0.000000 2 2.000000 [5 4 3 2 1 ] [5 4 3 2 1 ] 50 1 0 50 10 -1.000000 0.000000 4 4.000000 [5 4 3 2 1 ] [5 4 3 2 1 ] 50 3 0 100 20 -0.500000 0.000000 8 8.000000 [5 4 3 2 1 ] [5 4 3 2 1 ] 50 7 0 200 40 -0.250000 0.000000 16 16.000000 [5 4 3 2 1 ] [5 4 3 2 1 ] 50 15 0 400 80 +average since instance current true current predicted cur cur predic cache examples +loss last counter output prefix output prefix pass pol made hits gener beta +4.000000 4.000000 1 [5 4 3 2 1 ] [1 1 1 1 1 ] 0 0 5 0 25 0.000000 +2.000000 0.000000 2 [5 4 3 2 1 ] [5 4 3 2 1 ] 1 0 10 0 50 0.000000 +1.000000 0.000000 4 [5 4 3 2 1 ] [5 4 3 2 1 ] 3 0 20 0 100 0.000001 +0.500000 0.000000 8 [5 4 3 2 1 ] [5 4 3 2 1 ] 7 0 40 0 200 0.000002 +0.250000 0.000000 16 [5 4 3 2 1 ] [5 4 3 2 1 ] 15 0 80 0 400 0.000004 finished run number of examples per pass = 1 @@ -23,5 +21,5 @@ passes used = 20 weighted example sum = 20 weighted label sum = 0 average loss = 0.2 -best constant = -0.0526316 +best constant = 0 total feature number = 1000 diff --git a/test/train-sets/ref/sequence_data.nonldf.test.stderr b/test/train-sets/ref/sequence_data.nonldf.test.stderr index f2d1cd1c..c3409e1c 100644 --- a/test/train-sets/ref/sequence_data.nonldf.test.stderr +++ b/test/train-sets/ref/sequence_data.nonldf.test.stderr @@ -7,11 +7,9 @@ predictions = sequence_data.predict using no cache Reading datafile = train-sets/sequence_data num sources = 1 -average since example example current current current -loss last counter weight label predict features -average since sequence example current label current predicted current cur cur predic. examples -loss last counter weight sequence prefix sequence prefix features pass pol made gener. -0.000000 0.000000 1 1.000000 [5 4 3 2 1 ] [5 4 3 2 1 ] 15 0 0 5 0 +average since instance current true current predicted cur cur predic cache examples +loss last counter output prefix output prefix pass pol made hits gener beta +0.000000 0.000000 1 [5 4 3 2 1 ] [5 4 3 2 1 ] 0 0 5 0 0 0.000000 finished run number of examples per pass = 1 diff --git a/test/train-sets/ref/sequence_data.nonldf.train.stderr b/test/train-sets/ref/sequence_data.nonldf.train.stderr index d170aa86..2dbab703 100644 --- a/test/train-sets/ref/sequence_data.nonldf.train.stderr +++ b/test/train-sets/ref/sequence_data.nonldf.train.stderr @@ -7,15 +7,13 @@ decay_learning_rate = 1 creating cache_file = train-sets/sequence_data.cache Reading datafile = train-sets/sequence_data num sources = 1 -average since example example current current current -loss last counter weight label predict features -average since sequence example current label current predicted current cur cur predic. examples -loss last counter weight sequence prefix sequence prefix features pass pol made gener. -4.000000 4.000000 1 1.000000 [5 4 3 2 1 ] [1 1 1 1 1 ] 15 0 0 5 5 -4.000000 4.000000 2 2.000000 [5 4 3 2 1 ] [1 1 1 1 1 ] 15 1 0 10 10 -2.000000 0.000000 4 4.000000 [5 4 3 2 1 ] [5 4 3 2 1 ] 15 3 0 20 20 -1.000000 0.000000 8 8.000000 [5 4 3 2 1 ] [5 4 3 2 1 ] 15 7 0 40 40 -0.500000 0.000000 16 16.000000 [5 4 3 2 1 ] [5 4 3 2 1 ] 15 15 0 80 80 +average since instance current true current predicted cur cur predic cache examples +loss last counter output prefix output prefix pass pol made hits gener beta +4.000000 4.000000 1 [5 4 3 2 1 ] [1 1 1 1 1 ] 0 0 5 0 5 0.000000 +4.000000 4.000000 2 [5 4 3 2 1 ] [1 1 1 1 1 ] 1 0 10 0 10 0.000000 +2.000000 0.000000 4 [5 4 3 2 1 ] [5 4 3 2 1 ] 3 0 20 0 20 0.000000 +1.000000 0.000000 8 [5 4 3 2 1 ] [5 4 3 2 1 ] 7 0 40 0 40 0.000000 +0.500000 0.000000 16 [5 4 3 2 1 ] [5 4 3 2 1 ] 15 0 80 0 80 0.000001 finished run number of examples per pass = 1 diff --git a/test/train-sets/ref/sequencespan_data.nonldf-bilou.test.stderr b/test/train-sets/ref/sequencespan_data.nonldf-bilou.test.stderr index 7e008ec0..53c6b6ec 100644 --- a/test/train-sets/ref/sequencespan_data.nonldf-bilou.test.stderr +++ b/test/train-sets/ref/sequencespan_data.nonldf-bilou.test.stderr @@ -8,11 +8,9 @@ switching to BILOU encoding for sequence span labeling using no cache Reading datafile = train-sets/sequencespan_data num sources = 1 -average since example example current current current -loss last counter weight label predict features -average since sequence example current label current predicted current cur cur predic. examples -loss last counter weight sequence prefix sequence prefix features pass pol made gener. -0.000000 0.000000 1 1.000000 [2 1 1 2 2 1 6 7 7 ..] [2 1 1 2 2 1 6 7 7 ..] 45 0 0 15 0 +average since instance current true current predicted cur cur predic cache examples +loss last counter output prefix output prefix pass pol made hits gener beta +0.000000 0.000000 1 [2 1 1 2 2 1 6 7 7 ..] [2 1 1 2 2 1 6 7 7 ..] 0 0 15 0 0 0.000000 finished run number of examples per pass = 1 diff --git a/test/train-sets/ref/sequencespan_data.nonldf-bilou.train.stderr b/test/train-sets/ref/sequencespan_data.nonldf-bilou.train.stderr index bff3a58f..6b957e0e 100644 --- a/test/train-sets/ref/sequencespan_data.nonldf-bilou.train.stderr +++ b/test/train-sets/ref/sequencespan_data.nonldf-bilou.train.stderr @@ -8,21 +8,19 @@ switching to BILOU encoding for sequence span labeling creating cache_file = train-sets/sequencespan_data.cache Reading datafile = train-sets/sequencespan_data num sources = 1 -average since example example current current current -loss last counter weight label predict features -average since sequence example current label current predicted current cur cur predic. examples -loss last counter weight sequence prefix sequence prefix features pass pol made gener. -10.000000 10.000000 1 1.000000 [2 1 1 2 2 1 6 7 7 ..] [1 1 1 1 1 1 1 1 1 ..] 45 0 0 15 15 -7.500000 5.000000 2 2.000000 [2 1 1 2 2 1 6 7 7 ..] [2 1 1 6 4 1 6 7 7 ..] 45 1 0 30 30 -3.750000 0.000000 4 4.000000 [2 1 1 2 2 1 6 7 7 ..] [2 1 1 2 2 1 6 7 7 ..] 45 3 0 60 60 -1.875000 0.000000 8 8.000000 [2 1 1 2 2 1 6 7 7 ..] [2 1 1 2 2 1 6 7 7 ..] 45 7 0 120 120 -0.937500 0.000000 16 16.000000 [2 1 1 2 2 1 6 7 7 ..] [2 1 1 2 2 1 6 7 7 ..] 45 15 0 240 240 +average since instance current true current predicted cur cur predic cache examples +loss last counter output prefix output prefix pass pol made hits gener beta +6.000000 6.000000 1 [2 1 1 2 2 1 6 7 7 ..] [1 1 1 1 1 1 1 1 1 ..] 0 0 15 0 15 0.000000 +5.000000 4.000000 2 [2 1 1 2 2 1 6 7 7 ..] [2 1 1 6 4 1 6 7 7 ..] 1 0 30 0 30 0.000000 +2.500000 0.000000 4 [2 1 1 2 2 1 6 7 7 ..] [2 1 1 2 2 1 6 7 7 ..] 3 0 60 0 60 0.000001 +1.250000 0.000000 8 [2 1 1 2 2 1 6 7 7 ..] [2 1 1 2 2 1 6 7 7 ..] 7 0 120 0 120 0.000001 +0.625000 0.000000 16 [2 1 1 2 2 1 6 7 7 ..] [2 1 1 2 2 1 6 7 7 ..] 15 0 240 0 240 0.000002 finished run number of examples per pass = 1 passes used = 20 weighted example sum = 20 weighted label sum = 0 -average loss = 0.75 +average loss = 0.5 best constant = -0.0526316 total feature number = 900 diff --git a/test/train-sets/ref/sequencespan_data.nonldf.test.stderr b/test/train-sets/ref/sequencespan_data.nonldf.test.stderr index d52ea578..b21bbf15 100644 --- a/test/train-sets/ref/sequencespan_data.nonldf.test.stderr +++ b/test/train-sets/ref/sequencespan_data.nonldf.test.stderr @@ -7,11 +7,9 @@ predictions = sequencespan_data.predict using no cache Reading datafile = train-sets/sequencespan_data num sources = 1 -average since example example current current current -loss last counter weight label predict features -average since sequence example current label current predicted current cur cur predic. examples -loss last counter weight sequence prefix sequence prefix features pass pol made gener. -0.000000 0.000000 1 1.000000 [2 1 1 2 2 1 6 7 7 ..] [2 1 1 2 2 1 6 7 7 ..] 45 0 0 15 0 +average since instance current true current predicted cur cur predic cache examples +loss last counter output prefix output prefix pass pol made hits gener beta +0.000000 0.000000 1 [2 1 1 2 2 1 6 7 7 ..] [2 1 1 2 2 1 6 7 7 ..] 0 0 15 0 0 0.000000 finished run number of examples per pass = 1 diff --git a/test/train-sets/ref/sequencespan_data.nonldf.train.stderr b/test/train-sets/ref/sequencespan_data.nonldf.train.stderr index 649e61a5..1d7db07c 100644 --- a/test/train-sets/ref/sequencespan_data.nonldf.train.stderr +++ b/test/train-sets/ref/sequencespan_data.nonldf.train.stderr @@ -7,21 +7,19 @@ decay_learning_rate = 1 creating cache_file = train-sets/sequencespan_data.cache Reading datafile = train-sets/sequencespan_data num sources = 1 -average since example example current current current -loss last counter weight label predict features -average since sequence example current label current predicted current cur cur predic. examples -loss last counter weight sequence prefix sequence prefix features pass pol made gener. -10.000000 10.000000 1 1.000000 [2 1 1 2 2 1 6 7 7 ..] [1 1 1 1 1 1 1 1 1 ..] 45 0 0 15 15 -11.500000 13.000000 2 2.000000 [2 1 1 2 2 1 6 7 7 ..] [2 1 6 4 1 6 4 1 6 ..] 45 1 0 30 30 -7.500000 3.500000 4 4.000000 [2 1 1 2 2 1 6 7 7 ..] [2 1 1 2 2 1 6 7 7 ..] 45 3 0 60 60 -3.750000 0.000000 8 8.000000 [2 1 1 2 2 1 6 7 7 ..] [2 1 1 2 2 1 6 7 7 ..] 45 7 0 120 120 -1.875000 0.000000 16 16.000000 [2 1 1 2 2 1 6 7 7 ..] [2 1 1 2 2 1 6 7 7 ..] 45 15 0 240 240 +average since instance current true current predicted cur cur predic cache examples +loss last counter output prefix output prefix pass pol made hits gener beta +6.000000 6.000000 1 [2 1 1 2 2 1 6 7 7 ..] [1 1 1 1 1 1 1 1 1 ..] 0 0 15 0 15 0.000000 +8.500000 11.000000 2 [2 1 1 2 2 1 6 7 7 ..] [2 1 6 4 1 6 4 1 6 ..] 1 0 30 0 30 0.000000 +6.000000 3.500000 4 [2 1 1 2 2 1 6 7 7 ..] [2 1 1 2 2 1 6 7 7 ..] 3 0 60 0 60 0.000001 +3.000000 0.000000 8 [2 1 1 2 2 1 6 7 7 ..] [2 1 1 2 2 1 6 7 7 ..] 7 0 120 0 120 0.000001 +1.500000 0.000000 16 [2 1 1 2 2 1 6 7 7 ..] [2 1 1 2 2 1 6 7 7 ..] 15 0 240 0 240 0.000002 finished run number of examples per pass = 1 passes used = 20 weighted example sum = 20 weighted label sum = 0 -average loss = 1.5 +average loss = 1.2 best constant = -0.0526316 total feature number = 900 -- cgit v1.2.3