Interpreting Results
Benchmark results are available for every synthesizer and dataset pair. The returned results are a pandas DataFrame object.
Synthesizer Dataset Dataset_Size_MB Model_Time Peak_Memory_KB Model_Size_MB Sample_Time Evaluate_Time Diagnostic_Score Quality_Score NewRowSynthesis
GaussianCopulaSynthesizer alarm 34.5 123.56 300101 0.981 2012.1 1001.2 1.00000 0.9991991 0.998191
GaussianCopulaSynthesizer census 130.2 23546.12 201011 1.232 2012.2 101012.1 1.00000 0.689101 1.0
CTGANSynthesizer alarm 34.5 NaN 99999999 NaN NaN NaN 1.00000 NaN NaN
CTGANSynthesizer census 130.2 9919331 9929188110 12.10 123.31 NaN 1.00000 NaN NaN
IdentitySynthesizer alarm 34.5 0.00001 10 0.010 2012.2 1000 1.00000 1.0 0.0
IDentitySynthesizer census 130.2 2 2012.2 0.031 1003 0.321 1.00000 1.0 0.0
Returned Results
The results provide a summary of the benchmarking setup, performance during the execution and the overall evaluation. Browse through the tabs below to learn more about what each result means.
These results summarize the setup of your benchmarking run.
Synthesizer
: The name of the synthesizer used to model and create the synthetic dataDataset
: The name of the dataset that the synthesizer learned to createDataset_Size_MB
: The overall size of the dataset when loaded into Python, in MB
Errors
If the synthesizer crashed at any point in the process, you will see a NaN
value from that point onwards. For example, if your synthesizer ran out of memory during the training phase, you'll see NaN
values for the model size, sample time, evaluation time and other metrics.
FAQs
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