Introduction to Subsampling with @pandas_udf in PySpark

When working with large datasets in PySpark, it’s often necessary to perform subsampling or random sampling to reduce the amount of data being processed. One way to achieve this is by using the @pandas_udf decorator in combination with the train_test_split function from scikit-learn.

In this article, we’ll explore how to return multiple DataFrames using @pandas_udf in PySpark, and provide a step-by-step guide on how to achieve this.

Background: Understanding @pandas_udf

The @pandas_udf decorator is used to register a Pandas UDF (User-Defined Function) with PySpark. A Pandas UDF allows you to execute Python code in the context of a PySpark DataFrame, enabling you to perform complex data operations and transformations.

When using @pandas_udf, you need to specify the schema of your input DataFrame, as well as the type of operation being performed (e.g., GROUPED_MAP, which is used for grouped aggregation).

Subsampling with train_test_split

The train_test_split function from scikit-learn is commonly used for subsampling data in machine learning pipelines. It takes two main arguments: the feature data (X) and the target variable (Y), as well as a test size parameter that determines the proportion of data to be sampled.

However, when working with PySpark DataFrames, you need to use the train_test_split function from PySpark instead. This function returns four DataFrames: two training sets (one for features and one for labels) and two testing sets (one for features and one for labels).

Creating a Custom train_test_split Function

To create a custom train_test_split function using @pandas_udf, you need to specify the schema of your input DataFrame, as well as the type of operation being performed. In this case, we want to perform subsampling on the entire dataset.

Here’s an example code snippet that demonstrates how to create a custom train_test_split function using @pandas_udf:

@pandas_udf(schema, PandasUDFType.GROUPED_MAP)
def load_dataset(dataset):
    feature_columns = cols
    label = 'y';
    X = dataset[feature_columns]
    Y = dataset[label]

    # splitting the dataset into train and test
    X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2)

    # concatenating X_test and y_test to create a single DataFrame
    df_sample = pd.concat([X_test, y_test], axis=1)

    return (X_train, y_train), (X_test, y_test), (df_sample,)

Note that we’re returning four DataFrames: two training sets (one for features and one for labels) and two testing sets (one for features and one for labels).

Returning Multiple DataFrames

To return multiple DataFrames from a single @pandas_udf function, you can use the return_type parameter. This parameter specifies how to handle the output of the UDF, including whether to return individual DataFrames or a collection of DataFrames.

In our example code snippet, we’re using the return_type=ArrayType argument to specify that the UDF returns an array of DataFrames:

@pandas_udf(schema, PandasUDFType.GROUPED_MAP, return_type=ArrayType)
def load_dataset(dataset):
    ...

This allows us to return multiple DataFrames from a single function call.

Example Use Case

Here’s an example use case that demonstrates how to use the custom train_test_split function:

df = spark.createDataFrame([[1, 2], [3, 4], [5, 6]], ['A', 'B'])
result_df = load_dataset(df).apply((_, _, df_sample) => {
    return (df_sample)
})

In this example, we create a sample DataFrame df with two columns. We then call the custom train_test_split function on this DataFrame and pass in an arrow function that returns the df_sample DataFrame.

Conclusion

Subsampling is an important technique for working with large datasets in PySpark. By using the @pandas_udf decorator and the train_test_split function, you can create custom subsampling functions that return multiple DataFrames.

In this article, we’ve explored how to return multiple DataFrames using @pandas_udf in PySpark, including understanding the return_type parameter and creating custom subsampling functions.

Last modified on 2025-01-19