What's the difference between Spark ML and MLL

问题:

I noticed there are two LinearRegressionModel classes in SparkML, one in ML and another one in MLLib package.

These two are implemented quite differently - e.g. the one from MLLib implements Serializable, while the other one does not.

By the way ame is true about RandomForestModel.

Why is there two classes? Which is the "right" one? And is there a way to convert one into another?

回答1:

o.a.s.mllib contains old RDD-based API while o.a.s.ml contains new API build around Dataset and ML Pipelines. ml and mllib reached feature parity in 2.0.0 and mllib is slowly being deprecated (this already happened in case of linear regression) and most likely will be removed in the next major release.

So unless your goal is backward compatibility then the "right choice" is o.a.s.ml.

回答2:

Spark Mllib

spark.mllib contains the legacy API built on top of RDDs.

Spark ML

spark.ml provides higher-level API built on top of DataFrames for constructing ML pipelines.

According to [the official announcement

As of Spark 2.0, the RDD-based APIs in the spark.mllib package have entered maintenance mode. The primary Machine Learning API for Spark is now the DataFrame-based API in the spark.ml package. Apache spark is recommended to use spark.ml

• MLlib will still support the RDD-based API in spark.mllib with bug fixes.

• MLlib will not add new features to the RDD-based API.

• In the Spark 2.x releases, MLlib will add features to the DataFrames-based API to reach feature parity with the RDD-based API.

• After reaching feature parity (roughly estimated for Spark 2.3), the RDD-based API will be deprecated.
• The RDD-based API is expected to be removed in Spark 3.0.

Why is MLlib switching to the DataFrame-based API?

• DataFrames provide a more user-friendly API than RDDs. The many benefits of DataFrames include Spark Datasources, SQL/DataFrame queries, Tungsten and Catalyst optimizations, and uniform APIs across languages.

• The DataFrame-based API for MLlib provides a uniform API across ML algorithms and across multiple languages.

• DataFrames facilitate practical ML Pipelines, particularly feature transformations. See the Pipelines guide for details.

More info read doc -https://spark.apache.org/docs/latest/ml-guide.html