Comments (6)
Since the true data type was supposed to be numerical, I followed your first suggestion and just hardcoded it to numerical (as opposed to having it get detected which gave categorical). This resolved the issue.
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Hi there @prupireddy it looks like the reason the score isn't 100% is because the PARSynthesizer model isn't adhering to the min and max values in your original dataset column.
I have 2 questions:
-
When you crafted your SingleTableMetadata object, what
sdtype
was detected or did you assign? You can runprint(your_single_table_metdata_object)
on your machine to look this up and just tell me the one one for thisDays_Supplied
column. I'm also curious what pandas DataFrame dtype this column is (int
orfloat
)? -
Did you set the
enforce_min_max_values
parameter when defining your PARSynthesizer object? Or did you skip this and just left the model use the default value?
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- Categorical was detected (I didn't assign). The pandas dtype is float64.
- No I did not; it used the default.
Thank you
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Hi @prupireddy and @srinify there is currently a known issue that PARSynthesizer
specifically has when the column is categorical
but it is represented in a float
format. I wonder if this is the root cause? #1910.
I would start by confirming whether this column (Days_Supplied
) was correctly detected in the metadata, as the detection is not guaranteed to be 100% accurate. Does this column truly represent discrete categories or is it numerical? To help you decide, see this sdtypes reference.
- If it's supposed to be
numerical
, please update your metadata and try with the updated version. There are currently no known bugs in PARSynthesizer for numerical data. - If it's supposed to
categorical
, then you can try the workaround I've listed in #1910.
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Hi @prupireddy I noticed you closed the issue. Does that mean you were able to come up with a resolution?
For our knowledge (and perhaps to help others running into the same problem), you could clarify what the issue was?
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Great. Appreciate the confirmation!
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Related Issues (20)
- Synthetic time series data generated from PARSynthesizer is not ordered in time HOT 4
- How to evaluate the quality of synthetic time series data generated from PARSynthesizer HOT 3
- InvalidDataError when fitting datetime columns as context columns in PARSynthesizer HOT 3
- Primary keys may not be unique for variable length regexes HOT 4
- Add rename_column function to metadata API
- Add `utils` to the Top Level Package.
- When loading synthesizer from pkl, then trying to synthesizer.sample, I get: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters() HOT 1
- Cap boto and botocore
- Metadata.add_column can be slow
- Enable single table synthesizers to use new Metadata
- Enable multi table synthesizers to use new Metadata
- Enable evaluation methods to work with new metadata
- Update demos to use new metadata
- Foreign key references multiple tables HOT 5
- Add metadata anonymization to public SDV
- Unbalanced synthetic data with Uniform Encoder in child table with HMASynthesizer HOT 1
- Invalid Values PII HOT 1
- A large difference between the values ββin the child table HOT 2
- hma model gives one value in every column
- Review docs-related dev dependencies
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