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 should be designed as Pandas functions, meaning they must take input features as a Pandas Series and return either a Pandas Series or a Pandas DataFrame. Hopsworks automatically executes the defined transformation function as a pandas_udf in a PySpark application and as Pandas functions in Python clients.

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 has two arguments return_type and drop. The return_type is a mandatory argument and denotes the data types of the features returned by the transformation function. It can be a single Python type if the transformation function returns a single transformed feature or a list of Python types if it returns multiple transformed features. The supported types include str, int, float, bool, datetime.datetime, datetime.date, and datetime.time. The drop argument is optional and specifies the input arguments to remove from the final output after all transformation functions are applied. By default, all input arguments are retained in the final transformed output. The supported python types that be used with the return_type argument are provided as a table below

Hopsworks supports four types of transformation functions:

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 pd.DataFrame({"add_one":feature1 + 1, "add_two":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 pd.DataFrame({"add_one_feature1":feature1 + 1, "add_one_feature2":feature2 + 1, "add_one_feature3":feature3 + 1})

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 feature2 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 pd.DataFrame({"add_one_feature1":feature1 + 1, "add_one_feature2":feature2 + 1, "add_one_feature3":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