--save_resume bug prevents out-of-sample predictions

VowpalWabbit / vowpal_wabbit

Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

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(snorkel) Stephen-Hogg:Industry shogg$ vw -d ./train.json -c -k -f ./model_Agriculture__Forestry__Fishing.out --passes 10 --l2 0.0001 --classweight -1:0.03 --loss_function logistic --binary --holdout_off using l2 regularization = 0.0001 final_regressor = ./model_Agriculture__Forestry__Fishing.out parsed 1 class weights Num weight bits = 18 learning rate = 0.5 initial_t = 0 power_t = 0.5 decay_learning_rate = 1 creating cache_file = ./train.json.cache Reading datafile = ./train.json num sources = 1 average since example example current current current loss last counter weight label predict features 0.826446 0.826446 8 1.2 1.0000 -1.0000 331 0.827338 0.828025 28 2.8 1.0000 -1.0000 236 0.759936 0.706553 48 6.3 1.0000 1.0000 328 0.492958 0.259722 94 13.5 1.0000 1.0000 431 0.430398 0.369597 201 27.4 1.0000 1.0000 381 0.344580 0.261728 370 55.7 1.0000 1.0000 413 0.290409 0.236758 758 112.0 1.0000 1.0000 644 0.246606 0.203071 1539 224.6 1.0000 1.0000 1078 0.227734 0.208902 3094 449.8 1.0000 1.0000 408 0.211643 0.195554 6158 899.6 1.0000 1.0000 89 0.190891 0.170156 11855 1800.0 1.0000 1.0000 394 0.183195 0.175500 23840 3600.0 -1.0000 1.0000 572 0.170818 0.158443 47438 7200.5 1.0000 1.0000 449 0.161020 0.151223 93914 14401.2 1.0000 1.0000 739 0.154063 0.147105 187132 28802.8 1.0000 1.0000 278 0.145735 0.137408 374348 57606.1 1.0000 1.0000 550 0.147800 0.149864 748725 115213.1 1.0000 1.0000 289 0.153663 0.159526 1497838 230426.3 -1.0000 1.0000 180 finished run number of examples per pass = 195446 passes used = 10 weighted example sum = 300658.498857 weighted label sum = 198361.501143 average loss = 0.156028 best constant = 1.584766 best constant's loss = 0.456079 total feature number = 984538500 (snorkel) Stephen-Hogg:Industry shogg$ vw -d ./test.json -t -i ./model_Agriculture__Forestry__Fishing.out -p ./pred.out only testing predictions = ./pred.out Num weight bits = 18 learning rate = 0.5 initial_t = 0 power_t = 0.5 using no cache Reading datafile = ./test.json num sources = 1 average since example example current current current loss last counter weight label predict features 0.000000 0.000000 1 1.0 1.0000 1.0000 242 0.000000 0.000000 2 2.0 1.0000 1.0000 261 0.500000 1.000000 4 4.0 -1.0000 1.0000 463 0.500000 0.500000 8 8.0 -1.0000 1.0000 275 0.687500 0.875000 16 16.0 -1.0000 1.0000 201 0.593750 0.500000 32 32.0 -1.0000 1.0000 279 0.562500 0.531250 64 64.0 -1.0000 1.0000 238 0.515625 0.468750 128 128.0 -1.0000 1.0000 353 0.460938 0.406250 256 256.0 1.0000 1.0000 1014 0.429688 0.398438 512 512.0 1.0000 1.0000 466 0.440430 0.451172 1024 1024.0 -1.0000 1.0000 209 0.427734 0.415039 2048 2048.0 1.0000 1.0000 115 0.432861 0.437988 4096 4096.0 -1.0000 -1.0000 199 0.427856 0.422852 8192 8192.0 1.0000 1.0000 413 finished run number of examples = 12476 weighted example sum = 12476.000000 weighted label sum = 0.000000 average loss = 0.429465 best constant = 0.000000 best constant's loss = 1.000000 total feature number = 6057657

(snorkel) Stephen-Hogg:Industry shogg$ vw -d ./train.json -c -k -f ./model_Agriculture__Forestry__Fishing.out --passes 10 --l2 0.0001 --classweight -1:0.03 --loss_function logistic --binary --holdout_off --save_resume using l2 regularization = 0.0001 final_regressor = ./model_Agriculture__Forestry__Fishing.out parsed 1 class weights Num weight bits = 18 learning rate = 0.5 initial_t = 0 power_t = 0.5 decay_learning_rate = 1 creating cache_file = ./train.json.cache Reading datafile = ./train.json num sources = 1 average since example example current current current loss last counter weight label predict features 0.826446 0.826446 8 1.2 1.0000 -1.0000 331 0.827338 0.828025 28 2.8 1.0000 -1.0000 236 0.759936 0.706553 48 6.3 1.0000 1.0000 328 0.492958 0.259722 94 13.5 1.0000 1.0000 431 0.430398 0.369597 201 27.4 1.0000 1.0000 381 0.344580 0.261728 370 55.7 1.0000 1.0000 413 0.290409 0.236758 758 112.0 1.0000 1.0000 644 0.246606 0.203071 1539 224.6 1.0000 1.0000 1078 0.227734 0.208902 3094 449.8 1.0000 1.0000 408 0.211643 0.195554 6158 899.6 1.0000 1.0000 89 0.190891 0.170156 11855 1800.0 1.0000 1.0000 394 0.183195 0.175500 23840 3600.0 -1.0000 1.0000 572 0.170818 0.158443 47438 7200.5 1.0000 1.0000 449 0.161020 0.151223 93914 14401.2 1.0000 1.0000 739 0.154063 0.147105 187132 28802.8 1.0000 1.0000 278 0.145736 0.137409 374348 57606.1 1.0000 1.0000 550 0.147800 0.149864 748725 115213.1 1.0000 1.0000 289 0.153663 0.159527 1497838 230426.3 -1.0000 1.0000 180 finished run number of examples per pass = 195446 passes used = 10 weighted example sum = 300658.498857 weighted label sum = 198361.501143 average loss = 0.156029 best constant = 1.584766 best constant's loss = 0.456079 total feature number = 984538500 (snorkel) Stephen-Hogg:Industry shogg$ vw -d ./test.json -t -i ./model_Agriculture__Forestry__Fishing.out -p ./pred.out only testing predictions = ./pred.out Error: Disagreeing option values for '--l2_state': '0.237563858264723' vs '0.053069096623445' finished run number of examples = 0 weighted example sum = 0.000000 weighted label sum = 0.000000 average loss = n.a. total feature number = 0 Disagreeing option values for '--l2_state': '0.237563858264723' vs '0.053069096623445' vw (parser_helper.cc:172): Disagreeing option values for '--l2_state': '0.237563858264723' vs '0.053069096623445'

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--l2_state is an internal option which stores the not-yet-explicitly-applied effect of l2 regularization so that it can be applied later consistent with --save_resume. What's unclear here is how/why we are getting 2 different values for this.

On Tue, Aug 21, 2018 at 1:28 AM StephenHogg notifications@github.com wrote:

Sorry for the delay - here's some indicative fake data:

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echo '1 | x' | vw -k -f model.bin --save_resume --l2 1e-3 --passes 2 -c using l2 regularization = 0.001 final_regressor = model.bin Num weight bits = 18 learning rate = 0.5 initial_t = 0 power_t = 0.5 decay_learning_rate = 1 creating cache_file = .cache Reading datafile = num sources = 1 average since example example current current current loss last counter weight label predict features 1.000000 1.000000 1 1.0 1.0000 0.0000 2 unknown unknown 2 2.0 1.0000 0.5068 2 h finished run number of examples per pass = 1 passes used = 2 weighted example sum = 2.000000 weighted label sum = 2.000000 average loss = undefined (no holdout) best constant = 1.000000 best constant's loss = 0.000000 total feature number = 4 echo '1 | x' | vw -k -i model.bin --save_resume --l2 1e-3 --passes 2 -c using l2 regularization = 0.001 Error: Disagreeing option values for '--l2_state': '0.99964151809897' vs '0.999271369359276' finished run number of examples = 0 weighted example sum = 0.000000 weighted label sum = 0.000000 average loss = 0.000000 h total feature number = 0 Disagreeing option values for '--l2_state': '0.99964151809897' vs '0.999271369359276' vw (parser_helper.cc:172): Disagreeing option values for '--l2_state': '0.99964151809897' vs '0.999271369359276'```

VowpalWabbit / vowpal_wabbit

--save_resume bug prevents out-of-sample predictions #1573