Transformation Functions#

In AI systems, transformation functions transform data to create features, the inputs to machine learning models (in both training and inference). The taxonomy of data transformations introduces three types of data transformation prevalent in all AI systems. Hopsworks offers simple Python APIs to define custom transformation functions. These can be used along with feature groups and feature views to create on-demand transformations and model-dependent transformations, producing modular AI pipelines that are skew-free.

Custom Transformation Function Creation#

User-defined transformation functions can be created in Hopsworks using the @udf decorator. These functions can be either implemented as pure Python UDFs or Pandas UDFs (User-Defined Functions).

Hopsworks offers three execution modes to control the execution of transformation functions during training dataset creation, batch inference, and online inference. By default, Hopsworks executes transformation functions as Python UDFs for feature vector retrieval in online inference pipelines and as Pandas UDFs for both batch data retrieval in batch inference pipelines and training dataset creation in training pipelines. Python UDFs are optimized for smaller data volumes, while Pandas UDFs provide better performance on larger datasets. This execution mode provides the optimal balance based on the data size across training dataset generations, batch inference, and online inference. Additionally, Hopsworks allows you to explicitly set the execution mode for a transformation function to python or pandas, forcing the transformation function to always run as either a Python or Pandas UDF as specified.

A Pandas UDF in Hopsworks accepts one or more Pandas Series as input and can return either one or more Series or a Pandas DataFrame. When integrated with PySpark applications, Hopsworks automatically executes Pandas UDFs using PySpark’s pandas_udf, enabling the transformation functions to efficiently scale for large datasets.

Transformation functions created in Hopsworks can be directly attached to feature views or feature groups or stored in the feature store for later retrieval. These functions can be part of a library installed in Hopsworks or be defined in a Jupyter notebook running a Python kernel or added when starting a Jupyter notebook or Hopsworks job.

The @udf decorator in Hopsworks creates a metadata class called HopsworksUdf. This class manages the necessary operations to execute the transformation function.

The decorator accepts three parameters:

• return_type (required): Specifies the data type(s) of the features returned by the transformation function. It can be a single Python type if the function returns one transformed feature, or a list of Python types if it returns multiple transformed features. The supported Python types that be used with the return_type argument are provided in the table below:

  Supported Python Types
  str
  int
  float
  bool
  datetime.datetime
  datetime.date
  datetime.time

• drop (optional): Identifies input arguments to exclude from the output after transformations are applied. By default, all inputs are retained in the output. Further details on this argument can be found below.

• mode (optional): Determines the execution mode of the transformation function. The argument accepts three values: default, python, or pandas. By default, the mode is set to default. Further details on this argument can be found below.

Hopsworks supports four types of transformation functions across all execution modes:

1. One-to-one: Transforms one feature into one transformed feature.
2. One-to-many: Transforms one feature into multiple transformed features.
3. Many-to-one: Transforms multiple features into one transformed feature.
4. Many-to-many: Transforms multiple features into multiple transformed features.

One-to-one transformations#

To create a one-to-one transformation function, the Hopsworks @udf decorator must be provided with the return_type as a single Python type. The transformation function should take one argument as input and return a Pandas Series.

from hopsworks import udf

@udf(return_type=int)
def add_one(feature):
    return feature + 1

Many-to-one transformations#

The creation of many-to-one transformation functions is similar to that of a one-to-one transformation function, the only difference being that the transformation function accepts multiple features as input.

from hopsworks import udf

@udf(return_type=int)
def add_features(feature1, feature2, feature3):
    return feature + feature2 + feature3

One-to-many transformations#

To create a one-to-many transformation function, the Hopsworks @udf decorator must be provided with the return_type as a list of Python types, and the transformation function should take one argument as input and return multiple features as a Pandas DataFrame. The return types provided to the decorator must match the types of each column in the returned Pandas DataFrame.

from hopsworks import udf
import pandas as pd

@udf(return_type=[int, int])
def add_one_and_two(feature1):
    return feature1 + 1, feature1 + 2

Many-to-many transformations#

The creation of a many-to-many transformation function is similar to that of a one-to-many transformation function, the only difference being that the transformation function accepts multiple features as input.

from hopsworks import udf
import pandas as pd

@udf(return_type=[int, int, int])
def add_one_multiple(feature1, feature2, feature3):
    return feature1 + 1, feature2 + 1, feature3 + 1

Specifying execution modes#

The mode parameter of the @udf decorator can be used to specify the execution mode of the transformation function. It accepts three possible values default, python and pandas. Each mode is explained in more detail below:

Default#

This execution mode assumes that the transformation function can be executed as either a Pandas UDF or a Python UDF. It serves as the default mode used when the mode parameter is not specified. In this mode, the transformation function is executed as a Pandas UDF during training and in the batch inference pipeline, while it operates as a Python UDF during online inference.

from hopsworks import udf
import pandas as pd

# "default" mode is used if the parameter `mode` is not explicitly set.
@udf(return_type=[int, int, int])
def add_one_multiple(feature1, feature2, feature3):
    return feature1 + 1, feature2 + 1, feature3 + 1

@udf(return_type=[int, int, int], mode="default")
def add_two_multiple(feature1, feature2, feature3):
    return feature1 + 2, feature2 + 2, feature3 + 2

Python#

The transformation function can be configured to always execute as a Python UDF by setting the mode parameter of the @udf decorator to python.

from hopsworks import udf
import pandas as pd

@udf(return_type=[int, int, int], mode = "python")
def add_one_multiple(feature1, feature2, feature3):
    return feature1 + 1, feature2 + 1, feature3 + 1

Pandas#

The transformation function can be configured to always execute as a Pandas UDF by setting the mode parameter of the @udf decorator to pandas.

from hopsworks import udf
import pandas as pd

# A Pandas UDF returning a Pandas DataFrame
@udf(return_type=[int, int, int], mode = "pandas")
def add_one_multiple(feature1, feature2, feature3):
    return pd.DataFrame({"add_one_feature1":feature1 + 1, "add_one_feature2":feature2 + 1, "add_one_feature3":feature3 + 1})

# A Pandas UDF returning multiple Pandas Series
@udf(return_type=[int, int, int], mode="pandas")
def add_two_multiple(feature1, feature2, feature3):
    return feature1 + 2, feature2 + 2, feature3 + 2

Dropping input features#

The drop parameter of the @udf decorator is used to drop specific columns in the input DataFrame after transformation. If any argument of the transformation function is passed to the drop parameter, then the column mapped to the argument is dropped after the transformation functions are applied. In the example below, the columns mapped to the arguments feature1 and feature3 are dropped after the application of all transformation functions.

from hopsworks import udf
import pandas as pd

@udf(return_type=[int, int, int], drop=["feature1", "feature3"])
def add_one_multiple(feature1, feature2, feature3):
    return feature1 + 1, feature2 + 1, feature3 + 1

Training dataset statistics#

A keyword argument statistics can be defined in the transformation function if it requires training dataset statistics for any of its arguments. The statistics argument must be assigned an instance of the class TransformationStatistics as the default value. The TransformationStatistics instance must be initialized using the names of the arguments requiring statistics.

The TransformationStatistics instance contains separate objects with the same name as the arguments used to initialize it. These objects encapsulate statistics related to the argument as instances of the class FeatureTransformationStatistics. Upon instantiation, instances of FeatureTransformationStatistics contain None values and are updated with the required statistics after the creation of a training dataset.

from hopsworks import udf
from hopsworks.transformation_statistics import TransformationStatistics

stats = TransformationStatistics("argument1", "argument2", "argument3") 

@udf(int)
def add_features(argument1, argument2, argument3, statistics=stats):
    return argument + argument2 + argument3 + statistics.argument1.mean + statistics.argument2.mean + statistics.argument3.mean

Saving to the Feature Store#

To save a transformation function to the feature store, use the function create_transformation_function. It creates a TransformationFunction object which can then be saved by calling the save function. The save function will throw an error if another transformation function with the same name and version is already saved in the feature store.

plus_one_meta = fs.create_transformation_function(
            transformation_function=add_one,
            version=1)
plus_one_meta.save()

Retrieval from the Feature Store#

To retrieve all transformation functions from the feature store, use the function get_transformation_functions, which returns the list of TransformationFunction objects.

A specific transformation function can be retrieved using its name and version with the function get_transformation_function. If only the name is provided, then the version will default to 1.

# get all transformation functions
fs.get_transformation_functions()

# get transformation function by name. This will default to version 1
plus_one_fn = fs.get_transformation_function(name="plus_one")

# get transformation function by name and version.
plus_one_fn = fs.get_transformation_function(name="plus_one", version=2)

Using transformation functions#

Transformation functions can be used by attaching it to a feature view to create model-dependent transformations or attached to feature groups to create on-demand transformations