1. Application Development
2. Libraries
3. Machine Learning

FlinkML - Machine Learning for Flink

FlinkML is the Machine Learning (ML) library for Flink. It is a new effort in the Flink community, with a growing list of algorithms and contributors. With FlinkML we aim to provide scalable ML algorithms, an intuitive API, and tools that help minimize glue code in end-to-end ML systems. You can see more details about our goals and where the library is headed in our vision and roadmap here.

• Supported Algorithms
  • Supervised Learning
  • Unsupervised Learning
  • Data Preprocessing
  • Recommendation
  • Outlier selection
  • Utilities
• Getting Started
• Pipelines
• How to contribute

Supported Algorithms

FlinkML currently supports the following algorithms:

Supervised Learning

• SVM using Communication efficient distributed dual coordinate ascent (CoCoA)
• Multiple linear regression
• Optimization Framework

Unsupervised Learning

• k-Nearest neighbors join

Data Preprocessing

• Polynomial Features
• Standard Scaler
• MinMax Scaler

Recommendation

• Alternating Least Squares (ALS)

Outlier selection

• Stochastic Outlier Selection (SOS)

Utilities

• Distance Metrics
• Cross Validation

Getting Started

You can check out our quickstart guide for a comprehensive getting started example.

If you want to jump right in, you have to set up a Flink program. Next, you have to add the FlinkML dependency to the pom.xml of your project.

<dependency>
  <groupId>org.apache.flink</groupId>
  <artifactId>flink-ml_2.11</artifactId>
  <version>1.7.2</version>
</dependency>

Note that FlinkML is currently not part of the binary distribution. See linking with it for cluster execution here.

Now you can start solving your analysis task. The following code snippet shows how easy it is to train a multiple linear regression model.

// LabeledVector is a feature vector with a label (class or real value)
val trainingData: DataSet[LabeledVector] = ...
val testingData: DataSet[Vector] = ...

// Alternatively, a Splitter is used to break up a DataSet into training and testing data.
val dataSet: DataSet[LabeledVector] = ...
val trainTestData: DataSet[TrainTestDataSet] = Splitter.trainTestSplit(dataSet)
val trainingData: DataSet[LabeledVector] = trainTestData.training
val testingData: DataSet[Vector] = trainTestData.testing.map(lv => lv.vector)

val mlr = MultipleLinearRegression()
  .setStepsize(1.0)
  .setIterations(100)
  .setConvergenceThreshold(0.001)

mlr.fit(trainingData)

// The fitted model can now be used to make predictions
val predictions: DataSet[LabeledVector] = mlr.predict(testingData)

Pipelines

A key concept of FlinkML is its scikit-learn inspired pipelining mechanism. It allows you to quickly build complex data analysis pipelines how they appear in every data scientist’s daily work. An in-depth description of FlinkML’s pipelines and their internal workings can be found here.

The following example code shows how easy it is to set up an analysis pipeline with FlinkML.

val trainingData: DataSet[LabeledVector] = ...
val testingData: DataSet[Vector] = ...

val scaler = StandardScaler()
val polyFeatures = PolynomialFeatures().setDegree(3)
val mlr = MultipleLinearRegression()

// Construct pipeline of standard scaler, polynomial features and multiple linear regression
val pipeline = scaler.chainTransformer(polyFeatures).chainPredictor(mlr)

// Train pipeline
pipeline.fit(trainingData)

// Calculate predictions
val predictions: DataSet[LabeledVector] = pipeline.predict(testingData)

One can chain a Transformer to another Transformer or a set of chained Transformers by calling the method chainTransformer. If one wants to chain a Predictor to a Transformer or a set of chained Transformers, one has to call the method chainPredictor.

How to contribute

The Flink community welcomes all contributors who want to get involved in the development of Flink and its libraries. In order to get quickly started with contributing to FlinkML, please read our official contribution guide.