$$ \newcommand{\R}{\mathbb{R}} \newcommand{\E}{\mathbb{E}} \newcommand{\x}{\mathbf{x}} \newcommand{\y}{\mathbf{y}} \newcommand{\wv}{\mathbf{w}} \newcommand{\av}{\mathbf{\alpha}} \newcommand{\bv}{\mathbf{b}} \newcommand{\N}{\mathbb{N}} \newcommand{\id}{\mathbf{I}} \newcommand{\ind}{\mathbf{1}} \newcommand{\0}{\mathbf{0}} \newcommand{\unit}{\mathbf{e}} \newcommand{\one}{\mathbf{1}} \newcommand{\zero}{\mathbf{0}} \newcommand\rfrac[2]{^{#1}\!/_{#2}} \newcommand{\norm}[1]{\left\lVert#1\right\rVert} $$

Quickstart Guide

Introduction

FlinkML is designed to make learning from your data a straight-forward process, abstracting away the complexities that usually come with big data learning tasks. In this quick-start guide we will show just how easy it is to solve a simple supervised learning problem using FlinkML. But first some basics, feel free to skip the next few lines if you’re already familiar with Machine Learning (ML).

As defined by Murphy [1] ML deals with detecting patterns in data, and using those learned patterns to make predictions about the future. We can categorize most ML algorithms into two major categories: Supervised and Unsupervised Learning.

  • Supervised Learning deals with learning a function (mapping) from a set of inputs (features) to a set of outputs. The learning is done using a training set of (input, output) pairs that we use to approximate the mapping function. Supervised learning problems are further divided into classification and regression problems. In classification problems we try to predict the class that an example belongs to, for example whether a user is going to click on an ad or not. Regression problems one the other hand, are about predicting (real) numerical values, often called the dependent variable, for example what the temperature will be tomorrow.

  • Unsupervised Learning deals with discovering patterns and regularities in the data. An example of this would be clustering, where we try to discover groupings of the data from the descriptive features. Unsupervised learning can also be used for feature selection, for example through principal components analysis.

Linking with FlinkML

In order to use FlinkML in your project, first 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_2.10</artifactId>
  <version>1.3-SNAPSHOT</version>
</dependency>

Loading data

To load data to be used with FlinkML we can use the ETL capabilities of Flink, or specialized functions for formatted data, such as the LibSVM format. For supervised learning problems it is common to use the LabeledVector class to represent the (label, features) examples. A LabeledVector object will have a FlinkML Vector member representing the features of the example and a Double member which represents the label, which could be the class in a classification problem, or the dependent variable for a regression problem.

As an example, we can use Haberman’s Survival Data Set , which you can download from the UCI ML repository. This dataset “contains cases from a study conducted on the survival of patients who had undergone surgery for breast cancer”. The data comes in a comma-separated file, where the first 3 columns are the features and last column is the class, and the 4th column indicates whether the patient survived 5 years or longer (label 1), or died within 5 years (label 2). You can check the UCI page for more information on the data.

We can load the data as a DataSet[String] first:

import org.apache.flink.api.scala._

val env = ExecutionEnvironment.getExecutionEnvironment

val survival = env.readCsvFile[(String, String, String, String)]("/path/to/haberman.data")

We can now transform the data into a DataSet[LabeledVector]. This will allow us to use the dataset with the FlinkML classification algorithms. We know that the 4th element of the dataset is the class label, and the rest are features, so we can build LabeledVector elements like this:

import org.apache.flink.ml.common.LabeledVector
import org.apache.flink.ml.math.DenseVector

val survivalLV = survival
  .map{tuple =>
    val list = tuple.productIterator.toList
    val numList = list.map(_.asInstanceOf[String].toDouble)
    LabeledVector(numList(3), DenseVector(numList.take(3).toArray))
  }

We can then use this data to train a learner. We will however use another dataset to exemplify building a learner; that will allow us to show how we can import other dataset formats.

LibSVM files

A common format for ML datasets is the LibSVM format and a number of datasets using that format can be found in the LibSVM datasets website. FlinkML provides utilities for loading datasets using the LibSVM format through the readLibSVM function available through the MLUtils object. You can also save datasets in the LibSVM format using the writeLibSVM function. Let’s import the svmguide1 dataset. You can download the training set here and the test set here. This is an astroparticle binary classification dataset, used by Hsu et al. [3] in their practical Support Vector Machine (SVM) guide. It contains 4 numerical features, and the class label.

We can simply import the dataset then using:

import org.apache.flink.ml.MLUtils

val astroTrain: DataSet[LabeledVector] = MLUtils.readLibSVM(env, "/path/to/svmguide1")
val astroTest: DataSet[(Vector, Double)] = MLUtils.readLibSVM(env, "/path/to/svmguide1.t")
      .map(x => (x.vector, x.label))

This gives us two DataSet objects that we will use in the following section to create a classifier.

Classification

Once we have imported the dataset we can train a Predictor such as a linear SVM classifier. We can set a number of parameters for the classifier. Here we set the Blocks parameter, which is used to split the input by the underlying CoCoA algorithm [2] uses. The regularization parameter determines the amount of $l_2$ regularization applied, which is used to avoid overfitting. The step size determines the contribution of the weight vector updates to the next weight vector value. This parameter sets the initial step size.

import org.apache.flink.ml.classification.SVM

val svm = SVM()
  .setBlocks(env.getParallelism)
  .setIterations(100)
  .setRegularization(0.001)
  .setStepsize(0.1)
  .setSeed(42)

svm.fit(astroTrain)

We can now make predictions on the test set, and use the evaluate function to create (truth, prediction) pairs.

val evaluationPairs: DataSet[(Double, Double)] = svm.evaluate(astroTest)

Next we will see how we can pre-process our data, and use the ML pipelines capabilities of FlinkML.

Data pre-processing and pipelines

A pre-processing step that is often encouraged [3] when using SVM classification is scaling the input features to the [0, 1] range, in order to avoid features with extreme values dominating the rest. FlinkML has a number of Transformers such as MinMaxScaler that are used to pre-process data, and a key feature is the ability to chain Transformers and Predictors together. This allows us to run the same pipeline of transformations and make predictions on the train and test data in a straight-forward and type-safe manner. You can read more on the pipeline system of FlinkML in the pipelines documentation.

Let us first create a normalizing transformer for the features in our dataset, and chain it to a new SVM classifier.

import org.apache.flink.ml.preprocessing.MinMaxScaler

val scaler = MinMaxScaler()

val scaledSVM = scaler.chainPredictor(svm)

We can now use our newly created pipeline to make predictions on the test set. First we call fit again, to train the scaler and the SVM classifier. The data of the test set will then be automatically scaled before being passed on to the SVM to make predictions.

scaledSVM.fit(astroTrain)

val evaluationPairsScaled: DataSet[(Double, Double)] = scaledSVM.evaluate(astroTest)

The scaled inputs should give us better prediction performance.

Where to go from here

This quickstart guide can act as an introduction to the basic concepts of FlinkML, but there’s a lot more you can do. We recommend going through the FlinkML documentation, and trying out the different algorithms. A very good way to get started is to play around with interesting datasets from the UCI ML repository and the LibSVM datasets. Tackling an interesting problem from a website like Kaggle or DrivenData is also a great way to learn by competing with other data scientists. If you would like to contribute some new algorithms take a look at our contribution guide.

References

[1] Murphy, Kevin P. Machine learning: a probabilistic perspective. MIT press, 2012.

[2] Jaggi, Martin, et al. Communication-efficient distributed dual coordinate ascent. Advances in Neural Information Processing Systems. 2014.

[3] Hsu, Chih-Wei, Chih-Chung Chang, and Chih-Jen Lin. A practical guide to support vector classification. 2003.