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[SPARK-14503][ML] spark.ml API for FPGrowth

Yuhao authored 8 years ago

## What changes were proposed in this pull request?

jira: https://issues.apache.org/jira/browse/SPARK-14503
Function parity: Add FPGrowth and AssociationRules to ML.

design doc: https://docs.google.com/document/d/1bVhABn5DiEj8bw0upqGMJT2L4nvO_0_cXdwu4uMT6uU/pub

Currently I make FPGrowthModel a transformer. For each association rule,  it will just examine the input items against antecedents and summarize the consequents.

Update:
Thinking again, FPGrowth is only the algorithm to find the frequent itemsets, and can be replaced by other algorithms. The frequent itemsets are used by AssociationRules to generate the association rules. Then we can use the association rules to predict with other records.

![drawing1](https://cloud.githubusercontent.com/assets/7981698/22489294/76b9302c-e7cb-11e6-8d2d-3fc53f407b2f.png)

**For reviewers**, Let's first decide if the current `transform` function meets your expectation.

Current options:

1. Current implementation: Use Estimator and Transformer pattern in ML, the `transform` function will examine the input items against all the association rules and summarize the consequents. Users can also access frequent items and association rules via other model members.

2. Keep the Estimator and Transformer pattern. But AssociationRulesModel and FPGrowthModel will have empty `transform` function, meaning DataFrame has no change after transform. But users can access frequent items and association rules via other model members.

3. (mentioned by zhengruifeng) Keep the Estimator and Transformer pattern. But `FPGrowthModel` and `AssociationRulesModel` will just return frequent itemsets and association rules DataFrame in the `transform` function. Meaning the resulting DataFrame after `transform` will not be related to the input DataFrame.

4. Discard the Estimator and Transformer pattern. Both FPGrowth and FPGrowthModel will directly extend from PipelineStage, thus we don't need to have a `transform` function.

 I'd like to hear more concrete suggestions. I would prefer option 1 or 2.

update 2:

As discussed  in the jira, we will not expose AssociationRules as a public API for now.

## How was this patch tested?

new unit test suites

Author: Yuhao <yuhao.yang@intel.com>
Author: Yuhao Yang <yuhao.yang@intel.com>
Author: Yuhao Yang <hhbyyh@gmail.com>

Closes #15415 from hhbyyh/mlfpm.

0fe8020f

0fe8020f 8 years ago

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Apache Spark

Spark is a fast and general cluster computing system for Big Data. 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 Spark Streaming for stream processing.

http://spark.apache.org/

Online Documentation

You can find the latest Spark documentation, including a programming guide, on the project web page. This README file only contains basic setup instructions.

Building Spark

Spark is built using Apache Maven. To build Spark and its example programs, run:

build/mvn -DskipTests clean package

(You do not need to do this if you downloaded a pre-built package.)

You can build Spark using more than one thread by using the -T option with Maven, see "Parallel builds in Maven 3". More detailed documentation is available from the project site, at "Building Spark".

For general development tips, including info on developing Spark using an IDE, see "Useful Developer Tools".

Interactive Scala Shell

The easiest way to start using Spark is through the Scala shell:

./bin/spark-shell

Try the following command, which should return 1000:

scala> sc.parallelize(1 to 1000).count()

Interactive Python Shell

Alternatively, if you prefer Python, you can use the Python shell:

./bin/pyspark

And run the following command, which should also return 1000:

>>> sc.parallelize(range(1000)).count()

Example Programs

Spark also comes with several sample programs in the examples directory. To run one of them, use ./bin/run-example <class> [params]. For example:

./bin/run-example SparkPi

will run the Pi example locally.

You can set the MASTER environment variable when running examples to submit examples to a cluster. This can be a mesos:// or spark:// URL, "yarn" to run on YARN, and "local" to run locally with one thread, or "local[N]" to run locally with N threads. You can also use an abbreviated class name if the class is in the examples package. For instance:

MASTER=spark://host:7077 ./bin/run-example SparkPi

Many of the example programs print usage help if no params are given.

Running Tests

Testing first requires building Spark. Once Spark is built, tests can be run using:

./dev/run-tests

Please see the guidance on how to run tests for a module, or individual tests.

A Note About Hadoop Versions

Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs.

Please refer to the build documentation at "Specifying the Hadoop Version" for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions.

Configuration

Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.

## Contributing

Please review the Contribution to Spark guide for information on how to get started contributing to the project.