Static Parameters in Feature Functions

[NaeemS]

Hi,

I'm implementing a machine learning pipeline using feature stores and I'm running into a limitation with feature functions. I'd like to perform multiple calculations on my columns with some minor adjustments, but I need to pass a static parameter to control the behavior of the function.

Can I pass a static parameter to a feature function to control its behavior? For example, I want to aggregate values from an array and pass an index as a condition up to which I want to sum. This index would be a static parameter, not a value inside a column. Can I do this using a single function instead of defining a different functions for each condition?

Additionally, is it possible to use a single function to perform operations like sum on different numbers of columns (e.g., sum 2 columns, sum 3 columns, etc.)?

Thanks for your help!

[VZLA]

Hi @NaeemS thanks for your question!

Yes, you can pass a static parameter to a feature function to control its behavior in Databricks Feature Store. This allows you to perform multiple calculations on your columns with minor adjustments without defining different functions for each condition. To achieve this, you can use the FeatureFunction object in the databricks.feature_engineering package. Here’s an example of how you can define a feature function that takes a static parameter:

from databricks.feature_engineering import FeatureFunction, FeatureEngineeringClient

# Define the feature function
@udf(returnType=IntegerType())
def sum_up_to_index(array, index):
    return sum(array[:index])

# Create the FeatureFunction object
sum_up_to_index_fn = FeatureFunction(
    udf_name="main.default.sum_up_to_index",
    output_name="sum_result",
    input_bindings={"array": "array_column", "index": 5}  # Static parameter
)

# Create a FeatureSpec with the feature function
fe = FeatureEngineeringClient()
features = [sum_up_to_index_fn]
fe.create_feature_spec(name="main.default.array_features", features=features)

In this example, the sum_up_to_index function sums the values in an array up to a specified index, which is passed as a static parameter.

Regarding your second question, it is possible to use a single function to perform operations like sum on different numbers of columns. You can achieve this by defining a generic feature function and using it with different input bindings. Here’s an example:

# Define the feature function for summing columns
@udf(returnType=IntegerType())
def sum_columns(*cols):
    return sum(cols)

# Create FeatureFunction objects for different sets of columns
sum_2_columns_fn = FeatureFunction(
    udf_name="main.default.sum_columns",
    output_name="sum_2_columns",
    input_bindings={"cols": ["col1", "col2"]}
)

sum_3_columns_fn = FeatureFunction(
    udf_name="main.default.sum_columns",
    output_name="sum_3_columns",
    input_bindings={"cols": ["col1", "col2", "col3"]}
)

# Create a FeatureSpec with the feature functions
features = [sum_2_columns_fn, sum_3_columns_fn]
fe.create_feature_spec(name="main.default.column_sums", features=features)

In this example, the sum_columns function is used to sum different sets of columns by specifying different input bindings.

These approaches allow you to create flexible and reusable feature functions in Databricks Feature Store