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## What changes were proposed in this pull request? **Main change**: Added save/load for RandomForestClassifier, RandomForestRegressor (implementation details below) Modified numTrees method (*deprecation*) * Goal: Use default implementations of unit tests which assume Estimators and Models share the same set of Params. * What this PR does: Moves method numTrees outside of trait TreeEnsembleModel. Adds it to GBT and RF Models. Deprecates it in RF Models in favor of new method getNumTrees. In Spark 2.1, we can have RF Models include Param numTrees. Minor items * Fixes bugs in GBTClassificationModel, GBTRegressionModel fromOld methods where they assign the wrong old UID. **Implementation details** * Split DecisionTreeModelReadWrite.loadTreeNodes into 2 methods in order to reuse some code for ensembles. * Added EnsembleModelReadWrite object with save/load implementations usable for RFs and GBTs * These store all trees' nodes in a single DataFrame, and all trees' metadata in a second DataFrame. * Split trait RandomForestParams into parts in order to add more Estimator Params to RF models * Split DefaultParamsWriter.saveMetadata into two methods to allow ensembles to store sub-models' metadata in a single DataFrame. Same for DefaultParamsReader.loadMetadata ## How was this patch tested? Adds standard unit tests for RF save/load Author: Joseph K. Bradley <joseph@databricks.com> Author: GayathriMurali <gayathri.m.softie@gmail.com> Closes #12118 from jkbradley/GayathriMurali-SPARK-13784. |
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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.
Online Documentation
You can find the latest Spark documentation, including a programming guide, on the project web page and project wiki. 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.) More detailed documentation is available from the project site, at "Building Spark". For developing Spark using an IDE, see Eclipse and IntelliJ.
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.