[FSTORE-1285] Model Dependent Transformation Functions #390
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kennethmhc
requested changes
Jun 13, 2024
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| You can also create new functions. Let's assume that you have already installed Python library [transformation_fn_template](https://github.com/logicalclocks/transformation_fn_template) containing the transformation function `plus_one`. | ||
| The `@udf` decorator in Hopsworks creates a metadata class called `HopsworksUdf`. This class manages the necessary operations to supply feature statistics to custom transformation functions and execute them as `@pandas_udf` in PySpark applications or as pure Pandas functions in Python clients. The decorator requires the `return_type` of the transformation function, which indicates the type of features returned. This can be a single Python type if the transformation function returns a single transformed feature as a Pandas Series, or a list of Python types if it returns multiple transformed features as a Pandas DataFrame. The supported types include `str`, `int`, `float`, `bool`, `datetime.datetime`, `datetime.date`, and `datetime.time`. |
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would be nice to provide a link to the api reference of hopsworks udf
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added API reference to udf
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version should be a variable {{{ hopsworks_version }}}
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Used {{{hopsworks_version}}} in the link.
kennethmhc
requested changes
Jun 17, 2024
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| You can also create new functions. Let's assume that you have already installed Python library [transformation_fn_template](https://github.com/logicalclocks/transformation_fn_template) containing the transformation function `plus_one`. | ||
| The `@udf` decorator in Hopsworks creates a metadata class called `HopsworksUdf`. This class manages the necessary operations to supply feature statistics to custom transformation functions and execute them as `@pandas_udf` in PySpark applications or as pure Pandas functions in Python clients. The decorator requires the `return_type` of the transformation function, which indicates the type of features returned. This can be a single Python type if the transformation function returns a single transformed feature as a Pandas Series, or a list of Python types if it returns multiple transformed features as a Pandas DataFrame. The supported types include `str`, `int`, `float`, `bool`, `datetime.datetime`, `datetime.date`, and `datetime.time`. |
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version should be a variable {{{ hopsworks_version }}}
kennethmhc
approved these changes
Jun 18, 2024
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This PR contains the documentation of Model Dependent Transformation Functions implemented as part of PR's
JIRA : https://hopsworks.atlassian.net/browse/FSTORE-1285?atlOrigin=eyJpIjoiYjYwZDAyM2ZjZjNkNDU5MWEyNmRjN2MxZjNiZmNhZmEiLCJwIjoiaiJ9