Remove model_missing_values from ClusterBasedNormalizer call
#310
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fealho
requested review from
amontanez24 and
frances-h
and removed request for
a team and
amontanez24
amontanez24
reviewed
Aug 16, 2023
ctgan/data_transformer.py
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| @@ -46,7 +46,7 @@ def _fit_continuous(self, data): | |||
| A ``ColumnTransformInfo`` object. | |||
| """ | |||
| column_name = data.columns[0] | |||
| gm = ClusterBasedNormalizer(model_missing_values=True, max_clusters=min(len(data), 10)) | |||
| gm = ClusterBasedNormalizer(max_clusters=min(len(data), 10)) | |||
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to keep the behavior the same I think we have to change it to missing_value_generation='from_column'. The default is to fill with random nans
frances-h
approved these changes
Aug 16, 2023
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In RDT's latest version
model_missing_valuesis being deprecated.Resolve SDV-1544