34a889db85
I Implemented the KMeans API for spark.ml Pipelines. But it doesn't include clustering abstractions for spark.ml (SPARK-7610). It would fit for another issues. And I'll try it later, since we are trying to add the hierarchical clustering algorithms in another issue. Thanks. [SPARK-7879] KMeans API for spark.ml Pipelines - ASF JIRA https://issues.apache.org/jira/browse/SPARK-7879 Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com> Closes #6756 from yu-iskw/SPARK-7879 and squashes the following commits: be752de [Yu ISHIKAWA] Add assertions a14939b [Yu ISHIKAWA] Fix the dashed line's length in pyspark.ml.rst 4c61693 [Yu ISHIKAWA] Remove the test about whether "features" and "prediction" columns exist or not in Python fb2417c [Yu ISHIKAWA] Use getInt, instead of get f397be4 [Yu ISHIKAWA] Switch the comparisons. ca78b7d [Yu ISHIKAWA] Add the Scala docs about the constraints of each parameter. effc650 [Yu ISHIKAWA] Using expertSetParam and expertGetParam c8dc6e6 [Yu ISHIKAWA] Remove an unnecessary test 19a9d63 [Yu ISHIKAWA] Include spark.ml.clustering to python tests 1abb19c [Yu ISHIKAWA] Add the statements about spark.ml.clustering into pyspark.ml.rst f8338bc [Yu ISHIKAWA] Add the placeholders in Python 4a03003 [Yu ISHIKAWA] Test for contains in Python 6566c8b [Yu ISHIKAWA] Use `get`, instead of `apply` 288e8d5 [Yu ISHIKAWA] Using `contains` to check the column names 5a7d574 [Yu ISHIKAWA] Renamce `validateInitializationMode` to `validateInitMode` and remove throwing exception 97cfae3 [Yu ISHIKAWA] Fix the type of return value of `KMeans.copy` e933723 [Yu ISHIKAWA] Remove the default value of seed from the Model class 978ee2c [Yu ISHIKAWA] Modify the docs of KMeans, according to mllib's KMeans 2ec80bc [Yu ISHIKAWA] Fit on 1 line e186be1 [Yu ISHIKAWA] Make a few variables, setters and getters be expert ones b2c205c [Yu ISHIKAWA] Rename the method `getInitializationSteps` to `getInitSteps` and `setInitializationSteps` to `setInitSteps` in Scala and Python f43f5b4 [Yu ISHIKAWA] Rename the method `getInitializationMode` to `getInitMode` and `setInitializationMode` to `setInitMode` in Scala and Python 3cb5ba4 [Yu ISHIKAWA] Modify the description about epsilon and the validation 4fa409b [Yu ISHIKAWA] Add a comment about the default value of epsilon 2f392e1 [Yu ISHIKAWA] Make some variables `final` and Use `IntParam` and `DoubleParam` 19326f8 [Yu ISHIKAWA] Use `udf`, instead of callUDF 4d2ad1e [Yu ISHIKAWA] Modify the indentations 0ae422f [Yu ISHIKAWA] Add a test for `setParams` 4ff7913 [Yu ISHIKAWA] Add "ml.clustering" to `javacOptions` in SparkBuild.scala 11ffdf1 [Yu ISHIKAWA] Use `===` and the variable 220a176 [Yu ISHIKAWA] Set a random seed in the unit testing 92c3efc [Yu ISHIKAWA] Make the points for a test be fewer c758692 [Yu ISHIKAWA] Modify the parameters of KMeans in Python 6aca147 [Yu ISHIKAWA] Add some unit testings to validate the setter methods 687cacc [Yu ISHIKAWA] Alias mllib.KMeans as MLlibKMeans in KMeansSuite.scala a4dfbef [Yu ISHIKAWA] Modify the last brace and indentations 5bedc51 [Yu ISHIKAWA] Remve an extra new line 444c289 [Yu ISHIKAWA] Add the validation for `runs` e41989c [Yu ISHIKAWA] Modify how to validate `initStep` 7ea133a [Yu ISHIKAWA] Change how to validate `initMode` 7991e15 [Yu ISHIKAWA] Add a validation for `k` c2df35d [Yu ISHIKAWA] Make `predict` private 93aa2ff [Yu ISHIKAWA] Use `withColumn` in `transform` d3a79f7 [Yu ISHIKAWA] Remove the inhefited docs e9532e1 [Yu ISHIKAWA] make `parentModel` of KMeansModel private 8559772 [Yu ISHIKAWA] Remove the `paramMap` parameter of KMeans 6684850 [Yu ISHIKAWA] Rename `initializationSteps` to `initSteps` 99b1b96 [Yu ISHIKAWA] Rename `initializationMode` to `initMode` 79ea82b [Yu ISHIKAWA] Modify the parameters of KMeans docs 6569bcd [Yu ISHIKAWA] Change how to set the default values with `setDefault` 20a795a [Yu ISHIKAWA] Change how to set the default values with `setDefault` 11c2a12 [Yu ISHIKAWA] Limit the imports badb481 [Yu ISHIKAWA] Alias spark.mllib.{KMeans, KMeansModel} f80319a [Yu ISHIKAWA] Rebase mater branch and add copy methods 85d92b1 [Yu ISHIKAWA] Add `KMeans.setPredictionCol` aa9469d [Yu ISHIKAWA] Fix a python test suite error caused by python 3.x c2d6bcb [Yu ISHIKAWA] ADD Java test suites of the KMeans API for spark.ml Pipeline 598ed2e [Yu ISHIKAWA] Implement the KMeans API for spark.ml Pipelines in Python 63ad785 [Yu ISHIKAWA] Implement the KMeans API for spark.ml Pipelines in Scala |
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examples | ||
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launcher | ||
mllib | ||
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README.md | ||
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tox.ini |
Apache Spark
Spark is a fast and general cluster computing system for Big Data. It provides high-level APIs in Scala, Java, and Python, 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".
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-cluster" or "yarn-client" 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. See also "Third Party Hadoop Distributions" for guidance on building a Spark application that works with a particular distribution.
Configuration
Please refer to the Configuration guide in the online documentation for an overview on how to configure Spark.