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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2018/01/17 18:45:00 UTC
[jira] [Commented] (SPARK-23011) Support alternative function form
with group aggregate pandas UDF
[ https://issues.apache.org/jira/browse/SPARK-23011?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16329177#comment-16329177 ]
Apache Spark commented on SPARK-23011:
--------------------------------------
User 'icexelloss' has created a pull request for this issue:
https://github.com/apache/spark/pull/20295
> Support alternative function form with group aggregate pandas UDF
> -----------------------------------------------------------------
>
> Key: SPARK-23011
> URL: https://issues.apache.org/jira/browse/SPARK-23011
> Project: Spark
> Issue Type: Sub-task
> Components: PySpark
> Affects Versions: 2.3.0
> Reporter: Li Jin
> Priority: Major
>
> The current semantics of groupby apply is that the output schema of groupby apply is the same as the output schema of the UDF. Because grouping column is usually useful to users, users often need to output grouping columns in the UDF. To further explain, consider the following example:
> {code:java}
> import statsmodels.api as sm
> # df has four columns: id, y, x1, x2
> group_column = 'id'
> y_column = 'y'
> x_columns = ['x1', 'x2']
> schema = df.select(group_column, *x_columns).schema
> @pandas_udf(schema, PandasUDFType.GROUP_MAP)
> # Input/output are both a pandas.DataFrame
> def ols(pdf):
> group_key = pdf[group_column].iloc[0]
> y = pdf[y_column]
> X = pdf[x_columns]
> X = sm.add_constant(X)
> model = sm.OLS(y, X).fit()
> return pd.DataFrame([[group_key] + [model.params[i] for i in x_columns]], columns=[group_column] + x_columns)
> beta = df.groupby(group_column).apply(ols)
> {code}
> Although the UDF (linear regression) has nothing to do with the grouping column, the user needs to deal with grouping column in the UDF. In other words, the UDF is tightly coupled with the grouping column.
>
> With discussion in [https://github.com/apache/spark/pull/20211#discussion_r160524679,] we reached consensus for supporting an alternative function form:
> {code:java}
> def foo(key, pdf):
> key # this is a grouping key.
> pdf # this is the Pandas DataFrame
> pudf = pandas_udf(f=foo, returnType="id int, v double", functionType=GROUP_MAP)
> df.groupby(group_column).apply(pudf){code}
> {code:java}
> import statsmodels.api as sm
> # df has four columns: id, y, x1, x2
> group_column = 'id'
> y_column = 'y'
> x_columns = ['x1', 'x2']
> schema = df.select(group_column, *x_columns).schema
> @pandas_udf(schema, PandasUDFType.GROUP_MAP)
> # Input/output are both a pandas.DataFrame
> def ols(key, pdf):
> y = pdf[y_column]
> X = pdf[x_columns]
> X = sm.add_constant(X)
> model = sm.OLS(y, X).fit()
> return pd.DataFrame([key + [model.params[i] for i in x_columns]])
> beta = df.groupby(group_column).apply(ols)
> {code}
>
> In summary:
> * Support alternative form f(key, pdf). The current form f(pdf) will still be supported. (Through function inspection)
> * In both cases, the udf output schema will be the final output schema of the spark DataFrame.
> * Key will be passed to user as a python tuple.
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