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This is part (1a) of the updates from the design doc in [https://docs.google.com/document/d/1BH9el33kBX8JiDdgUJXdLW14CA2qhTCWIG46eXZVoJs] **UPDATE**: Most of the APIs are being kept private[spark] to allow further discussion. Here is a list of changes which are public: * new output columns: rawPrediction, probabilities * The “score” column is now called “rawPrediction” * Classifiers now provide numClasses * Params.get and .set are now protected instead of private[ml]. * ParamMap now has a size method. * new classes: LinearRegression, LinearRegressionModel * LogisticRegression now has an intercept. ### Sketch of APIs (most of which are private[spark] for now) Abstract classes for learning algorithms (+ corresponding Model abstractions): * Classifier (+ ClassificationModel) * ProbabilisticClassifier (+ ProbabilisticClassificationModel) * Regressor (+ RegressionModel) * Predictor (+ PredictionModel) * *For all of these*: * There is no strongly typed training-time API. * There is a strongly typed test-time (prediction) API which helps developers implement new algorithms. Concrete classes: learning algorithms * LinearRegression * LogisticRegression (updated to use new abstract classes) * Also, removed "score" in favor of "probability" output column. Changed BinaryClassificationEvaluator to match. (SPARK-5031) Other updates: * params.scala: Changed Params.set/get to be protected instead of private[ml] * This was needed for the example of defining a class from outside of the MLlib namespace. * VectorUDT: Will later change from private[spark] to public. * This is needed for outside users to write their own validateAndTransformSchema() methods using vectors. * Also, added equals() method.f * SPARK-4942 : ML Transformers should allow output cols to be turned on,off * Update validateAndTransformSchema * Update transform * (Updated examples, test suites according to other changes) New examples: * DeveloperApiExample.scala (example of defining algorithm from outside of the MLlib namespace) * Added Java version too Test Suites: * LinearRegressionSuite * LogisticRegressionSuite * + Java versions of above suites CC: mengxr etrain shivaram Author: Joseph K. Bradley <joseph@databricks.com> Closes #3637 from jkbradley/ml-api-part1 and squashes the following commits: 405bfb8 [Joseph K. Bradley] Last edits based on code review. Small cleanups fec348a [Joseph K. Bradley] Added JavaDeveloperApiExample.java and fixed other issues: Made developer API private[spark] for now. Added constructors Java can understand to specialized Param types. 8316d5e [Joseph K. Bradley] fixes after rebasing on master fc62406 [Joseph K. Bradley] fixed test suites after last commit bcb9549 [Joseph K. Bradley] Fixed issues after rebasing from master (after move from SchemaRDD to DataFrame) 9872424 [Joseph K. Bradley] fixed JavaLinearRegressionSuite.java Java sql api f542997 [Joseph K. Bradley] Added MIMA excludes for VectorUDT (now public), and added DeveloperApi annotation to it 216d199 [Joseph K. Bradley] fixed after sql datatypes PR got merged f549e34 [Joseph K. Bradley] Updates based on code review. Major ones are: * Created weakly typed Predictor.train() method which is called by fit() so that developers do not have to call schema validation or copy parameters. * Made Predictor.featuresDataType have a default value of VectorUDT. * NOTE: This could be dangerous since the FeaturesType type parameter cannot have a default value. 343e7bd [Joseph K. Bradley] added blanket mima exclude for ml package 82f340b [Joseph K. Bradley] Fixed bug in LogisticRegression (introduced in this PR). Fixed Java suites 0a16da9 [Joseph K. Bradley] Fixed Linear/Logistic RegressionSuites c3c8da5 [Joseph K. Bradley] small cleanup 934f97b [Joseph K. Bradley] Fixed bugs from previous commit. 1c61723 [Joseph K. Bradley] * Made ProbabilisticClassificationModel into a subclass of ClassificationModel. Also introduced ProbabilisticClassifier. * This was to support output column “probabilityCol” in transform(). 4e2f711 [Joseph K. Bradley] rat fix bc654e1 [Joseph K. Bradley] Added spark.ml LinearRegressionSuite 8d13233 [Joseph K. Bradley] Added methods: * Classifier: batch predictRaw() * Predictor: train() without paramMap ProbabilisticClassificationModel.predictProbabilities() * Java versions of all above batch methods + others 1680905 [Joseph K. Bradley] Added JavaLabeledPointSuite.java for spark.ml, and added constructor to LabeledPoint which defaults weight to 1.0 adbe50a [Joseph K. Bradley] * fixed LinearRegression train() to use embedded paramMap * added Predictor.predict(RDD[Vector]) method * updated Linear/LogisticRegressionSuites 58802e3 [Joseph K. Bradley] added train() to Predictor subclasses which does not take a ParamMap. 57d54ab [Joseph K. Bradley] * Changed semantics of Predictor.train() to merge the given paramMap with the embedded paramMap. * remove threshold_internal from logreg * Added Predictor.copy() * Extended LogisticRegressionSuite e433872 [Joseph K. Bradley] Updated docs. Added LabeledPointSuite to spark.ml 54b7b31 [Joseph K. Bradley] Fixed issue with logreg threshold being set correctly 0617d61 [Joseph K. Bradley] Fixed bug from last commit (sorting paramMap by parameter names in toString). Fixed bug in persisting logreg data. Added threshold_internal to logreg for faster test-time prediction (avoiding map lookup). 601e792 [Joseph K. Bradley] Modified ParamMap to sort parameters in toString. Cleaned up classes in class hierarchy, before implementing tests and examples. d705e87 [Joseph K. Bradley] Added LinearRegression and Regressor back from ml-api branch 52f4fde [Joseph K. Bradley] removing everything except for simple class hierarchy for classification d35bb5d [Joseph K. Bradley] fixed compilation issues, but have not added tests yet bfade12 [Joseph K. Bradley] Added lots of classes for new ML API: |
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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, 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 structured data processing, 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:
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 all automated 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.