h2o.deeplearning: model object output needs a fix

[exalate-issue-sync[bot]]

When run default params and print the model object, lists incorrect and unnecessary info- like epoch counter =0, training sample =0, training time =0 ntr = h2o.importFile(h,"/Users/nidhimehta/Desktop/german/trn.csv", key="ntr") ntr myX = 1:20 myY = 21 ntr$C21 = as.factor(ntr$C21) gg = h2o.deeplearning(myX,myY,training_frame=ntr) H2OBinomialModel: deeplearning

Model Details:

Epoch Counter [1] 0

Training Samples [1] 0

Training Time (milliseconds) [1] 0

Score for training samples [1] 0

Score for validation samples [1] 0

Model summary Status of Neuron Layers:

Units Type Dropout L1 L2 Rate (Mean,RMS)

1 1 61 Input 0.00 %
2 2 200 Rectifier 0.00 % 0.000000 0.000000 (0.00430193, 0.00285227) 3 3 200 Rectifier 0.00 % 0.000000 0.000000 (0.0840693, 0.183056) 4 4 2 Softmax 0.000000 0.000000 (0.00243884, 0.00170867) Weight (Mean,RMS) Bias (Mean,RMS) 1
2 (-0.0104380, 0.0930086) (0.422794, 0.0357473) 3 (-0.00797316, 0.0715604) (0.968534, 0.0298924) 4 (0.0131957, 0.377100) (-6.30273e-07, 0.00626674)

Whether the model is an autoencoder [1] FALSE

Frame keys for weight matrices NULL

Frame keys for bias vectors NULL

Scoring history Scoring History: Timestamp Training Duration Training Speed Training Epochs Training Samples 1 2015-04-13 16:22:36 0.000 sec 0.00000 0 2 2015-04-13 16:22:36 0.076 sec 9210.526 rows/sec 1.00000 700 3 2015-04-13 16:22:37 0.618 sec 11326.861 rows/sec 10.00000 7000 Training MSE Training R^2 Training LogLoss Training AUC Training Classification Error 1
2 0.21309 -0.02168 0.71938 0.80123 0.26857 3 0.16068 0.22959 0.49460 0.95010 0.11571

Training data model metrics $__meta $__meta$schema_version [1] 3

$__meta$schema_name [1] "ModelMetricsBinomialV3"

$__meta$schema_type [1] "ModelMetricsBinomial"

$model $model$__meta $model$__meta$schema_version [1] 1

$model$__meta$schema_name [1] "ModelKeyV1"

$model$__meta$schema_type [1] "Key"

$model$name [1] "DeepLearningModel__a1f4d448a7312967931324490fb44df7"

$model$type [1] "Key"

$model$URL [1] "/3/Models/DeepLearningModel__a1f4d448a7312967931324490fb44df7"

$model_checksum [1] -4.312644e+17

$frame $frame$__meta $frame$__meta$schema_version [1] 1

$frame$__meta$schema_name [1] "FrameKeyV1"

$frame$__meta$schema_type [1] "Key"

$frame$name [1] "ntr.DeepLearningModel__a1f4d448a7312967931324490fb44df7.train.chunks32"

$frame$type [1] "Key"

$frame$URL [1] "/3/Frames/ntr.DeepLearningModel__a1f4d448a7312967931324490fb44df7.train.chunks32"

$frame_checksum [1] 7.136369e+14

$description [1] "Metrics reported on 700 training set samples"

$model_category [1] "Binomial"

$duration_in_ms [1] 0

$scoring_time [1] 0

$predictions NULL

$mse [1] 0.1606798

$sigma [1] 0.4566895

$logloss [1] 0.494598

$AUC [1] 0.9500985

$Gini [1] 0.900197

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0.7172557 0.3880765 0.4171429 0.0261668994401892 0.0261668994401892 0.5000000 0.7142857 0.3846154 0.4085714 0.023801440373063087 0.023801440373063087 0.4987952 0.7133012 0.3834754 0.4057143 0.022190519049763678 0.022190519049763678 0.4958084 0.7108516 0.3806547 0.3985714 0.019813889637589455 0.019813889637589455 0.4934446 0.7089041 0.3784278 0.3928571 0.017269440926611423 0.017269440926611423 0.4911032 0.7069672 0.3762268 0.3871429 0.014993454702198506 0.014993454702198506 0.4882075 0.7045609 0.3735114 0.3800000 0.013857729732990265 0.013857729732990265 0.4864865 0.7031250 0.3719008 0.3757143 0.012099358998239041 0.012099358998239041 0.4836449 0.7007448 0.3692472 0.3685714 0.01045650034211576 0.01045650034211576 0.4813953 0.6988521 0.3671515 0.3628571 0.008841608185321093 0.008841608185321093 0.4780600 0.6960323 0.3640521 0.3542857 0.007844964973628521 0.007844964973628521 0.4753157 0.6936997 0.3615089 0.3471429 0.005684102326631546 0.005684102326631546 0.4726027 0.6913828 0.3590010 0.3400000 0.004445107498516639 0.004445107498516639 0.4709898 0.6900000 0.3575130 0.3357143 0.003363913099747151 0.003363913099747151 0.4667418 0.6863395 0.3536044 0.3242857 0.0016700075357221067 0.0016700075357221067 0.4605117 0.6809211 0.3478992 0.3071429 6.067733474992565E-4 6.067733474992565E-4 0.4564498 0.6773560 0.3441969 0.2957143 precision recall specificity absolute_MCC minPerClassCorrect tps 0.9995637834072113 1.0000000 0.1062802 1.00000000 0.27799359 0.10628019 22 0.9985599347523281 1.0000000 0.1739130 1.00000000 0.35933995 0.17391304 36 0.9972188882529736 1.0000000 0.2512077 1.00000000 0.43717212 0.25120773 52 0.9959982534249624 1.0000000 0.3091787 1.00000000 0.48955300 0.30917874 64 0.9952171274593898 1.0000000 0.3429952 1.00000000 0.51849209 0.34299517 71 0.9943989430155072 1.0000000 0.3768116 1.00000000 0.54650029 0.37681159 78 0.9924641946951548 1.0000000 0.4057971 1.00000000 0.56988546 0.40579710 84 0.99098316166136 0.9892473 0.4444444 0.99797160 0.59484482 0.44444444 92 0.9889334780829293 0.9800000 0.4734300 0.99594320 0.61214070 0.47342995 98 0.9880543649196625 0.9811321 0.5024155 0.99594320 0.63446036 0.50241546 104 0.9867485761642456 0.9724771 0.5120773 0.99391481 0.63686207 0.51207729 106 0.9841835796833038 0.9734513 0.5314010 0.99391481 0.65158459 0.53140097 110 0.9816154340902964 0.9747899 0.5603865 0.99391481 0.67343079 0.56038647 116 0.9774053891499838 0.9672131 0.5700483 0.99188641 0.67600618 0.57004831 118 0.9745887666940689 0.9682540 0.5893720 0.99188641 0.69045702 0.58937198 122 0.9721342399716377 0.9477612 0.6135266 0.98580122 0.69520427 0.61352657 127 0.9695497393608093 0.9352518 0.6280193 0.98174442 0.69755973 0.62801932 130 0.9649120171864828 0.9225352 0.6328502 0.97768763 0.69288310 0.63285024 131 0.9626012444496155 0.9230769 0.6376812 0.97768763 0.69654388 0.63768116 132 0.9593709111213684 0.9241379 0.6473430 0.97768763 0.70384890 0.64734300 134 0.9578045457601547 0.9194631 0.6618357 0.97565923 0.71074934 0.66183575 137 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0.1036817654967308 333 0.10021499916911125 335 0.09624096378684044 337 0.09446882953246434 340 0.0899050310254097 341 0.08733144775032997 343 0.08458234369754791 346 0.08020112663507462 350 0.07751592248678207 351 0.07355566322803497 352 0.07079328844944636 355 0.0676176194101572 359 0.0643180176615715 364 0.06078796274960041 366 0.05720316618680954 367 0.052226098254323006 371 0.04891185089945793 374 0.04715742543339729 376 0.045270126312971115 380 0.04312439188361168 385 0.04023090377449989 388 0.036808644980192186 393 0.03489669486880302 398 0.03269428635636965 401 0.029936549067497254 406 0.028215221129357815 408 0.0261668994401892 414 0.023801440373063087 416 0.022190519049763678 421 0.019813889637589455 425 0.017269440926611423 429 0.014993454702198506 434 0.013857729732990265 437 0.012099358998239041 442 0.01045650034211576 446 0.008841608185321093 452 0.007844964973628521 457 0.005684102326631546 462 0.004445107498516639 465 0.003363913099747151 473 0.0016700075357221067 485 6.067733474992565E-4 493

$max_criteria_and_metric_scores Metric Threshold Metric.1 idx 1 f1 0.8662049373 0.8076010 48 2 f2 0.7288553715 0.8658205 81 3 f0point5 0.9745887667 0.8579466 14 4 accuracy 0.9082958400 0.8857143 37 5 precision 0.9995637834 1.0000000 0 6 recall 0.2587498724 1.0000000 175 7 specificity 0.9995637834 1.0000000 0 8 absolute_MCC 0.8662049373 0.7251011 48 9 minPerClassCorrect 0.8048728108 0.8681542 64 10 tps 0.2587498724 207.0000000 175 11 fps 0.0006067733 493.0000000 255

Validation data model metrics NULL

Variable Importances NULL

Training time (seconds) [1] 618

[exalate-issue-sync[bot]]

Arno Candel commented: Only weights, biases and variable importances are now in there.

[DinukaH2O]

JIRA Issue Migration Info

Jira Issue: PUBDEV-821 Assignee: Arno Candel Reporter: Nidhi Mehta State: Resolved Fix Version: N/A Attachments: N/A Development PRs: N/A