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### What changes were proposed in this pull request? Implement common base ML classes (`Predictor`, `PredictionModel`, `Classifier`, `ClasssificationModel` `ProbabilisticClassifier`, `ProbabilisticClasssificationModel`, `Regressor`, `RegrssionModel`) for non-Java backends. Note - `Predictor` and `JavaClassifier` should be abstract as `_fit` method is not implemented. - `PredictionModel` should be abstract as `_transform` is not implemented. ### Why are the changes needed? To provide extensions points for non-JVM algorithms, as well as a public (as opposed to `Java*` variants, which are commonly described in docstrings as private) hierarchy which can be used to distinguish between different classes of predictors. For longer discussion see [SPARK-29212](https://issues.apache.org/jira/browse/SPARK-29212) and / or https://github.com/apache/spark/pull/25776. ### Does this PR introduce any user-facing change? It adds new base classes as listed above, but effective interfaces (method resolution order notwithstanding) stay the same. Additionally "private" `Java*` classes in`ml.regression` and `ml.classification` have been renamed to follow PEP-8 conventions (added leading underscore). It is for discussion if the same should be done to equivalent classes from `ml.wrapper`. If we take `JavaClassifier` as an example, type hierarchy will change from  to  Similarly the old model  will become  ### How was this patch tested? Existing unit tests. Closes #27245 from zero323/SPARK-29212. Authored-by: zero323 <mszymkiewicz@gmail.com> Signed-off-by: zhengruifeng <ruifengz@foxmail.com> |
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| README.md | ||
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Apache Spark
Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
Online Documentation
You can find the latest Spark documentation, including a programming guide, on the project web page
Python Packaging
This README file only contains basic information related to pip installed PySpark. This packaging is currently experimental and may change in future versions (although we will do our best to keep compatibility). Using PySpark requires the Spark JARs, and if you are building this from source please see the builder instructions at "Building Spark".
The Python packaging for Spark is not intended to replace all of the other use cases. This Python packaged version of Spark is suitable for interacting with an existing cluster (be it Spark standalone, YARN, or Mesos) - but does not contain the tools required to set up your own standalone Spark cluster. You can download the full version of Spark from the Apache Spark downloads page.
NOTE: If you are using this with a Spark standalone cluster you must ensure that the version (including minor version) matches or you may experience odd errors.
Python Requirements
At its core PySpark depends on Py4J, but some additional sub-packages have their own extra requirements for some features (including numpy, pandas, and pyarrow).