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trainingdata2.log
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Inferring Columns ...
Creating Data loader ...
Loading data ...
Exploring multiple ML algorithms and settings to find you the best model for ML task: binary-classification
For further learning check: https://aka.ms/mlnet-cli
| Trainer Accuracy AUC AUPRC F1-score Duration #Iteration |
[Source=AutoML, Kind=Trace] Channel started
[Source=AutoML, Kind=Trace] Evaluating pipeline xf=TextFeaturizing{ col=Pierwszy_wynik_w_google__tf:Pierwszy_wynik_w_google_} xf=ColumnCopying{ col=Features:Pierwszy_wynik_w_google__tf} xf=Normalizing{ col=Features:Features} tr=AveragedPerceptronBinary{} cache=+
[Source=AutoML, Kind=Trace] 1 0,941469817711043 00:00:08.6324607 xf=TextFeaturizing{ col=Pierwszy_wynik_w_google__tf:Pierwszy_wynik_w_google_} xf=ColumnCopying{ col=Features:Pierwszy_wynik_w_google__tf} xf=Normalizing{ col=Features:Features} tr=AveragedPerceptronBinary{} cache=+
|1 AveragedPerceptronBinary 0,9415 0,9669 0,8376 0,8000 8,6 0 |
[Source=AutoML, Kind=Trace] Evaluating pipeline xf=TextFeaturizing{ col=Pierwszy_wynik_w_google__tf:Pierwszy_wynik_w_google_} xf=ColumnCopying{ col=Features:Pierwszy_wynik_w_google__tf} xf=Normalizing{ col=Features:Features} tr=SdcaLogisticRegressionBinary{} cache=+
[Source=AutoML, Kind=Trace] 2 0,948848132023459 00:00:13.6941818 xf=TextFeaturizing{ col=Pierwszy_wynik_w_google__tf:Pierwszy_wynik_w_google_} xf=ColumnCopying{ col=Features:Pierwszy_wynik_w_google__tf} xf=Normalizing{ col=Features:Features} tr=SdcaLogisticRegressionBinary{} cache=+
|2 SdcaLogisticRegressionBinary 0,9482 0,9841 0,9386 0,8214 13,7 0 |
[Source=AutoML, Kind=Trace] Evaluating pipeline xf=TextFeaturizing{ col=Pierwszy_wynik_w_google__tf:Pierwszy_wynik_w_google_} xf=ColumnCopying{ col=Features:Pierwszy_wynik_w_google__tf} tr=LightGbmBinary{} cache=-
[Source=AutoML, Kind=Trace] 3 0,950782410313589 00:00:29.7638745 xf=TextFeaturizing{ col=Pierwszy_wynik_w_google__tf:Pierwszy_wynik_w_google_} xf=ColumnCopying{ col=Features:Pierwszy_wynik_w_google__tf} tr=LightGbmBinary{} cache=-
|3 LightGbmBinary 0,9505 0,9520 0,9128 0,8525 29,8 0 |
[Source=AutoML, Kind=Trace] Evaluating pipeline xf=TextFeaturizing{ col=Pierwszy_wynik_w_google__tf:Pierwszy_wynik_w_google_} xf=ColumnCopying{ col=Features:Pierwszy_wynik_w_google__tf} xf=Normalizing{ col=Features:Features} tr=SymbolicSgdLogisticRegressionBinary{} cache=+
[Source=AutoML, Kind=Trace] 4 0,935403808279576 00:00:08.4699457 xf=TextFeaturizing{ col=Pierwszy_wynik_w_google__tf:Pierwszy_wynik_w_google_} xf=ColumnCopying{ col=Features:Pierwszy_wynik_w_google__tf} xf=Normalizing{ col=Features:Features} tr=SymbolicSgdLogisticRegressionBinary{} cache=+
|4 SymbolicSgdLogisticRegressionBinary 0,9378 0,9680 0,9070 0,8421 8,5 0 |
[Source=AutoML, Kind=Trace] Evaluating pipeline xf=TextFeaturizing{ col=Pierwszy_wynik_w_google__tf:Pierwszy_wynik_w_google_} xf=ColumnCopying{ col=Features:Pierwszy_wynik_w_google__tf} xf=Normalizing{ col=Features:Features} tr=LinearSvmBinary{} cache=+
Inferring Columns ...
Creating Data loader ...
Loading data ...
Exploring multiple ML algorithms and settings to find you the best model for ML task: binary-classification
For further learning check: https://aka.ms/mlnet-cli
| Trainer Accuracy AUC AUPRC F1-score Duration #Iteration |
[Source=AutoML, Kind=Trace] Channel started
[Source=AutoML, Kind=Trace] Evaluating pipeline xf=TextFeaturizing{ col=Pierwszy_wynik_w_google__tf:Pierwszy_wynik_w_google_} xf=ColumnCopying{ col=Features:Pierwszy_wynik_w_google__tf} xf=Normalizing{ col=Features:Features} tr=AveragedPerceptronBinary{} cache=+
[Source=AutoML, Kind=Trace] 1 0,92990300944011 00:00:08.1731553 xf=TextFeaturizing{ col=Pierwszy_wynik_w_google__tf:Pierwszy_wynik_w_google_} xf=ColumnCopying{ col=Features:Pierwszy_wynik_w_google__tf} xf=Normalizing{ col=Features:Features} tr=AveragedPerceptronBinary{} cache=+
|1 AveragedPerceptronBinary 0,9275 0,9479 0,8829 0,7742 8,2 0 |
[Source=AutoML, Kind=Trace] Evaluating pipeline xf=TextFeaturizing{ col=Pierwszy_wynik_w_google__tf:Pierwszy_wynik_w_google_} xf=ColumnCopying{ col=Features:Pierwszy_wynik_w_google__tf} xf=Normalizing{ col=Features:Features} tr=SdcaLogisticRegressionBinary{} cache=+
[Source=AutoML, Kind=Trace] 2 0,948848132023459 00:00:13.2947278 xf=TextFeaturizing{ col=Pierwszy_wynik_w_google__tf:Pierwszy_wynik_w_google_} xf=ColumnCopying{ col=Features:Pierwszy_wynik_w_google__tf} xf=Normalizing{ col=Features:Features} tr=SdcaLogisticRegressionBinary{} cache=+
|2 SdcaLogisticRegressionBinary 0,9482 0,9841 0,9386 0,8214 13,3 0 |
[Source=AutoML, Kind=Trace] Evaluating pipeline xf=TextFeaturizing{ col=Pierwszy_wynik_w_google__tf:Pierwszy_wynik_w_google_} xf=ColumnCopying{ col=Features:Pierwszy_wynik_w_google__tf} tr=LightGbmBinary{} cache=-
===============================================Experiment Results=================================================
------------------------------------------------------------------------------------------------------------------
| Summary |
------------------------------------------------------------------------------------------------------------------
|ML Task: binary-classification |
|Dataset: trainingdata2.tsv |
|Label : 0 |
|Total experiment time : 30,60 Secs |
|Total number of models explored: 2 |
------------------------------------------------------------------------------------------------------------------
| Top 2 models explored |
------------------------------------------------------------------------------------------------------------------
| Trainer Accuracy AUC AUPRC F1-score Duration #Iteration |
|1 SdcaLogisticRegressionBinary 0,9482 0,9841 0,9386 0,8214 13,3 2 |
|2 AveragedPerceptronBinary 0,9275 0,9479 0,8829 0,7742 8,2 1 |
------------------------------------------------------------------------------------------------------------------
Generated trained model for consumption: E:\Marcin\!Projekty\Eleia\tmp\SampleBinaryClassification\SampleBinaryClassification.Model\MLModel.zip
Generated C# code for model consumption: E:\Marcin\!Projekty\Eleia\tmp\SampleBinaryClassification\SampleBinaryClassification.ConsoleApp
Check out log file for more information: E:\Marcin\!Projekty\Eleia\tmp\SampleBinaryClassification\logs\debug_log.txt
Inferring Columns ...
Creating Data loader ...
Loading data ...
Exploring multiple ML algorithms and settings to find you the best model for ML task: binary-classification
For further learning check: https://aka.ms/mlnet-cli
| Trainer Accuracy AUC AUPRC F1-score Duration #Iteration |
[Source=AutoML, Kind=Trace] Channel started
[Source=AutoML, Kind=Trace] Evaluating pipeline xf=TextFeaturizing{ col=Pierwszy_wynik_w_google__tf:Pierwszy_wynik_w_google_} xf=ColumnCopying{ col=Features:Pierwszy_wynik_w_google__tf} xf=Normalizing{ col=Features:Features} tr=AveragedPerceptronBinary{} cache=+
[Source=AutoML, Kind=Trace] 1 0,935413246479253 00:00:08.3046172 xf=TextFeaturizing{ col=Pierwszy_wynik_w_google__tf:Pierwszy_wynik_w_google_} xf=ColumnCopying{ col=Features:Pierwszy_wynik_w_google__tf} xf=Normalizing{ col=Features:Features} tr=AveragedPerceptronBinary{} cache=+
|1 AveragedPerceptronBinary 0,9358 0,9544 0,8947 0,8182 8,3 0 |
[Source=AutoML, Kind=Trace] Evaluating pipeline xf=TextFeaturizing{ col=Pierwszy_wynik_w_google__tf:Pierwszy_wynik_w_google_} xf=ColumnCopying{ col=Features:Pierwszy_wynik_w_google__tf} xf=Normalizing{ col=Features:Features} tr=SdcaLogisticRegressionBinary{} cache=+
[Source=AutoML, Kind=Trace] 2 0,948848132023459 00:00:13.4824350 xf=TextFeaturizing{ col=Pierwszy_wynik_w_google__tf:Pierwszy_wynik_w_google_} xf=ColumnCopying{ col=Features:Pierwszy_wynik_w_google__tf} xf=Normalizing{ col=Features:Features} tr=SdcaLogisticRegressionBinary{} cache=+
|2 SdcaLogisticRegressionBinary 0,9482 0,9841 0,9386 0,8214 13,5 0 |
[Source=AutoML, Kind=Trace] Evaluating pipeline xf=TextFeaturizing{ col=Pierwszy_wynik_w_google__tf:Pierwszy_wynik_w_google_} xf=ColumnCopying{ col=Features:Pierwszy_wynik_w_google__tf} tr=LightGbmBinary{} cache=-
[Source=AutoML, Kind=Trace] 3 0,950782410313589 00:00:29.2573904 xf=TextFeaturizing{ col=Pierwszy_wynik_w_google__tf:Pierwszy_wynik_w_google_} xf=ColumnCopying{ col=Features:Pierwszy_wynik_w_google__tf} tr=LightGbmBinary{} cache=-
|3 LightGbmBinary 0,9505 0,9520 0,9128 0,8525 29,3 0 |
[Source=AutoML, Kind=Trace] Evaluating pipeline xf=TextFeaturizing{ col=Pierwszy_wynik_w_google__tf:Pierwszy_wynik_w_google_} xf=ColumnCopying{ col=Features:Pierwszy_wynik_w_google__tf} xf=Normalizing{ col=Features:Features} tr=SymbolicSgdLogisticRegressionBinary{} cache=+
[Source=AutoML, Kind=Trace] 4 0,93075200827201 00:00:08.2236469 xf=TextFeaturizing{ col=Pierwszy_wynik_w_google__tf:Pierwszy_wynik_w_google_} xf=ColumnCopying{ col=Features:Pierwszy_wynik_w_google__tf} xf=Normalizing{ col=Features:Features} tr=SymbolicSgdLogisticRegressionBinary{} cache=+
|4 SymbolicSgdLogisticRegressionBinary 0,9358 0,9519 0,9090 0,8286 8,2 0 |
[Source=AutoML, Kind=Trace] Evaluating pipeline xf=TextFeaturizing{ col=Pierwszy_wynik_w_google__tf:Pierwszy_wynik_w_google_} xf=ColumnCopying{ col=Features:Pierwszy_wynik_w_google__tf} xf=Normalizing{ col=Features:Features} tr=LinearSvmBinary{} cache=+
===============================================Experiment Results=================================================
------------------------------------------------------------------------------------------------------------------
| Summary |
------------------------------------------------------------------------------------------------------------------
|ML Task: binary-classification |
|Dataset: trainingdata2.tsv |
|Label : 0 |
|Total experiment time : 60,59 Secs |
|Total number of models explored: 4 |
------------------------------------------------------------------------------------------------------------------
| Top 4 models explored |
------------------------------------------------------------------------------------------------------------------
| Trainer Accuracy AUC AUPRC F1-score Duration #Iteration |
|1 LightGbmBinary 0,9505 0,9520 0,9128 0,8525 29,3 3 |
|2 SdcaLogisticRegressionBinary 0,9482 0,9841 0,9386 0,8214 13,5 2 |
|3 AveragedPerceptronBinary 0,9358 0,9544 0,8947 0,8182 8,3 1 |
|4 SymbolicSgdLogisticRegressionBinary 0,9358 0,9519 0,9090 0,8286 8,2 4 |
------------------------------------------------------------------------------------------------------------------
Generated trained model for consumption: E:\Marcin\!Projekty\Eleia\tmp\SampleBinaryClassification\SampleBinaryClassification.Model\MLModel.zip
Retrieving best pipeline ...
Generated C# code for model consumption: E:\Marcin\!Projekty\Eleia\tmp\SampleBinaryClassification\SampleBinaryClassification.ConsoleApp
Check out log file for more information: E:\Marcin\!Projekty\Eleia\tmp\SampleBinaryClassification\logs\debug_log.txt
Inferring Columns ...
Creating Data loader ...
Loading data ...
Exploring multiple ML algorithms and settings to find you the best model for ML task: binary-classification
For further learning check: https://aka.ms/mlnet-cli
| Trainer Accuracy AUC AUPRC F1-score Duration #Iteration |
[Source=AutoML, Kind=Trace] Channel started
[Source=AutoML, Kind=Trace] Evaluating pipeline xf=TextFeaturizing{ col=text_tf:text} xf=ColumnCopying{ col=Features:text_tf} xf=Normalizing{ col=Features:Features} tr=AveragedPerceptronBinary{} cache=+
[Source=AutoML, Kind=Trace] 1 0,94228461256824 00:00:08.2807485 xf=TextFeaturizing{ col=text_tf:text} xf=ColumnCopying{ col=Features:text_tf} xf=Normalizing{ col=Features:Features} tr=AveragedPerceptronBinary{} cache=+
|1 AveragedPerceptronBinary 0,9465 0,9352 0,8722 0,8077 8,3 0 |
[Source=AutoML, Kind=Trace] Evaluating pipeline xf=TextFeaturizing{ col=text_tf:text} xf=ColumnCopying{ col=Features:text_tf} xf=Normalizing{ col=Features:Features} tr=SdcaLogisticRegressionBinary{} cache=+
[Source=AutoML, Kind=Trace] 2 0,951553002034795 00:00:13.4604130 xf=TextFeaturizing{ col=text_tf:text} xf=ColumnCopying{ col=Features:text_tf} xf=Normalizing{ col=Features:Features} tr=SdcaLogisticRegressionBinary{} cache=+
|2 SdcaLogisticRegressionBinary 0,9572 0,9567 0,8803 0,8400 13,5 0 |
[Source=AutoML, Kind=Trace] Evaluating pipeline xf=TextFeaturizing{ col=text_tf:text} xf=ColumnCopying{ col=Features:text_tf} tr=LightGbmBinary{} cache=-
[Source=AutoML, Kind=Trace] 3 0,949105570086937 00:00:29.2662116 xf=TextFeaturizing{ col=text_tf:text} xf=ColumnCopying{ col=Features:text_tf} tr=LightGbmBinary{} cache=-
|3 LightGbmBinary 0,9447 0,9766 0,9286 0,8286 29,3 0 |
[Source=AutoML, Kind=Trace] Evaluating pipeline xf=TextFeaturizing{ col=text_tf:text} xf=ColumnCopying{ col=Features:text_tf} xf=Normalizing{ col=Features:Features} tr=SymbolicSgdLogisticRegressionBinary{} cache=+
[Source=AutoML, Kind=Trace] 4 0,920855889311687 00:00:08.1802031 xf=TextFeaturizing{ col=text_tf:text} xf=ColumnCopying{ col=Features:text_tf} xf=Normalizing{ col=Features:Features} tr=SymbolicSgdLogisticRegressionBinary{} cache=+
|4 SymbolicSgdLogisticRegressionBinary 0,9223 0,9301 0,8093 0,7273 8,2 0 |
[Source=AutoML, Kind=Trace] Evaluating pipeline xf=TextFeaturizing{ col=text_tf:text} xf=ColumnCopying{ col=Features:text_tf} xf=Normalizing{ col=Features:Features} tr=LinearSvmBinary{} cache=+
===============================================Experiment Results=================================================
------------------------------------------------------------------------------------------------------------------
| Summary |
------------------------------------------------------------------------------------------------------------------
|ML Task: binary-classification |
|Dataset: trainingdata2.tsv |
|Label : code |
|Total experiment time : 60,59 Secs |
|Total number of models explored: 4 |
------------------------------------------------------------------------------------------------------------------
| Top 4 models explored |
------------------------------------------------------------------------------------------------------------------
| Trainer Accuracy AUC AUPRC F1-score Duration #Iteration |
|1 SdcaLogisticRegressionBinary 0,9572 0,9567 0,8803 0,8400 13,5 2 |
|2 AveragedPerceptronBinary 0,9465 0,9352 0,8722 0,8077 8,3 1 |
|3 LightGbmBinary 0,9447 0,9766 0,9286 0,8286 29,3 3 |
|4 SymbolicSgdLogisticRegressionBinary 0,9223 0,9301 0,8093 0,7273 8,2 4 |
------------------------------------------------------------------------------------------------------------------
Generated trained model for consumption: E:\Marcin\!Projekty\Eleia\tmp\SampleBinaryClassification\SampleBinaryClassification.Model\MLModel.zip
Generated C# code for model consumption: E:\Marcin\!Projekty\Eleia\tmp\SampleBinaryClassification\SampleBinaryClassification.ConsoleApp
Check out log file for more information: E:\Marcin\!Projekty\Eleia\tmp\SampleBinaryClassification\logs\debug_log.txt
Inferring Columns ...
Creating Data loader ...
Loading data ...
Exploring multiple ML algorithms and settings to find you the best model for ML task: binary-classification
For further learning check: https://aka.ms/mlnet-cli
| Trainer Accuracy AUC AUPRC F1-score Duration #Iteration |
[Source=AutoML, Kind=Trace] Channel started
[Source=AutoML, Kind=Trace] Evaluating pipeline xf=TextFeaturizing{ col=text_tf:text} xf=ColumnCopying{ col=Features:text_tf} xf=Normalizing{ col=Features:Features} tr=AveragedPerceptronBinary{} cache=+
[Source=AutoML, Kind=Trace] 1 0,942887419445991 00:00:09.3781083 xf=TextFeaturizing{ col=text_tf:text} xf=ColumnCopying{ col=Features:text_tf} xf=Normalizing{ col=Features:Features} tr=AveragedPerceptronBinary{} cache=+
|1 AveragedPerceptronBinary 0,9430 0,9505 0,8663 0,8533 9,4 0 |
[Source=AutoML, Kind=Trace] Evaluating pipeline xf=TextFeaturizing{ col=text_tf:text} xf=ColumnCopying{ col=Features:text_tf} xf=Normalizing{ col=Features:Features} tr=SdcaLogisticRegressionBinary{} cache=+
[Source=AutoML, Kind=Trace] 2 0,951995479910901 00:00:13.9627582 xf=TextFeaturizing{ col=text_tf:text} xf=ColumnCopying{ col=Features:text_tf} xf=Normalizing{ col=Features:Features} tr=SdcaLogisticRegressionBinary{} cache=+
|2 SdcaLogisticRegressionBinary 0,9572 0,9565 0,8801 0,8400 14,0 0 |
[Source=AutoML, Kind=Trace] Evaluating pipeline xf=TextFeaturizing{ col=text_tf:text} xf=ColumnCopying{ col=Features:text_tf} tr=LightGbmBinary{} cache=-
[Source=AutoML, Kind=Trace] 3 0,949105570086937 00:00:37.0051141 xf=TextFeaturizing{ col=text_tf:text} xf=ColumnCopying{ col=Features:text_tf} tr=LightGbmBinary{} cache=-
|3 LightGbmBinary 0,9447 0,9766 0,9286 0,8286 37,0 0 |
[Source=AutoML, Kind=Trace] Evaluating pipeline xf=TextFeaturizing{ col=text_tf:text} xf=ColumnCopying{ col=Features:text_tf} xf=Normalizing{ col=Features:Features} tr=SymbolicSgdLogisticRegressionBinary{} cache=+
[Source=AutoML, Kind=Trace] 4 0,932309170703717 00:00:08.6897502 xf=TextFeaturizing{ col=text_tf:text} xf=ColumnCopying{ col=Features:text_tf} xf=Normalizing{ col=Features:Features} tr=SymbolicSgdLogisticRegressionBinary{} cache=+
|4 SymbolicSgdLogisticRegressionBinary 0,9275 0,9355 0,8209 0,7500 8,7 0 |
[Source=AutoML, Kind=Trace] Evaluating pipeline xf=TextFeaturizing{ col=text_tf:text} xf=ColumnCopying{ col=Features:text_tf} xf=Normalizing{ col=Features:Features} tr=LinearSvmBinary{} cache=+
[Source=AutoML, Kind=Trace] 5 0,927638290723382 00:00:07.8570099 xf=TextFeaturizing{ col=text_tf:text} xf=ColumnCopying{ col=Features:text_tf} xf=Normalizing{ col=Features:Features} tr=LinearSvmBinary{} cache=+
|5 LinearSvmBinary 0,9248 0,9125 0,8307 0,7671 7,9 0 |
[Source=AutoML, Kind=Trace] Evaluating pipeline xf=TextFeaturizing{ col=text_tf:text} xf=ColumnCopying{ col=Features:text_tf} tr=FastTreeBinary{} cache=-
===============================================Experiment Results=================================================
------------------------------------------------------------------------------------------------------------------
| Summary |
------------------------------------------------------------------------------------------------------------------
|ML Task: binary-classification |
|Dataset: trainingdata2.tsv |
|Label : code |
|Total experiment time : 180,57 Secs |
|Total number of models explored: 5 |
------------------------------------------------------------------------------------------------------------------
| Top 5 models explored |
------------------------------------------------------------------------------------------------------------------
| Trainer Accuracy AUC AUPRC F1-score Duration #Iteration |
|1 SdcaLogisticRegressionBinary 0,9572 0,9565 0,8801 0,8400 14,0 2 |
|2 LightGbmBinary 0,9447 0,9766 0,9286 0,8286 37,0 3 |
|3 AveragedPerceptronBinary 0,9430 0,9505 0,8663 0,8533 9,4 1 |
|4 SymbolicSgdLogisticRegressionBinary 0,9275 0,9355 0,8209 0,7500 8,7 4 |
|5 LinearSvmBinary 0,9248 0,9125 0,8307 0,7671 7,9 5 |
------------------------------------------------------------------------------------------------------------------
Generated trained model for consumption: E:\Marcin\!Projekty\Eleia\tmp\SampleBinaryClassification\SampleBinaryClassification.Model\MLModel.zip
Generated C# code for model consumption: E:\Marcin\!Projekty\Eleia\tmp\SampleBinaryClassification\SampleBinaryClassification.ConsoleApp
Check out log file for more information: E:\Marcin\!Projekty\Eleia\tmp\SampleBinaryClassification\logs\debug_log.txt