[box type=”note” align=”” class=”” width=””]We present to you an excerpt from our book by Dr. Uday Kamath and Krishna Choppella titled Mastering Java Machine Learning. This book aims to give you an array of advanced techniques on Machine Learning. [/box]
Our article given below talks about using H2O as a Machine Learning Platform for Big Data applications.
H2O is a leading open source platform for Machine Learning at Big Data scale, with a focus on bringing AI to the enterprise. The company counts several leading lights in statistical learning theory and optimization among its scientific advisors. It supports programming environments in multiple languages.
H2O architecture
The following figure gives a high-level architecture of H2O with important components. H2O can access data from various data stores such as HDFS, SQL, NoSQL, and Amazon S3, to name a few. The most popular deployment of H2O is to use one of the deployment stacks with Spark or to run it in a H2O cluster itself.
The core of H2O is an optimized way of handling Big Data in memory, so that iterative algorithms that go through the same data can be handled efficiently and achieve good performance. Important Machine Learning algorithms in supervised and unsupervised learning are implemented specially to handle horizontal scalability across multiple nodes and JVMs. H2O provides not only its own user interface, known as flow, to manage and run modeling tasks, but also has different language bindings and connector APIs to Java, R, Python, and Scala.
Most Machine Learning algorithms, optimization algorithms, and utilities use the concept of fork-join or MapReduce. As shown in the figure below, the entire dataset is considered as a Data Frame in H2O, and comprises vectors, which are features or columns in the dataset. The rows or instances are made up of one element from each Vector arranged side-by-side. The rows are grouped together to form a processing unit known as a Chunk. Several chunks are combined in one JVM. Any algorithmic or optimization work begins by sending the information from the topmost JVM to fork on to the next JVM, then on to the next, and so on, similar to the map operation in MapReduce. Each JVM works on the rows in the chunks to establish the task and finally the results flow back in the reduce operation:
Machine learning in H2O
The following figure shows all the Machine Learning algorithms supported in H2O v3 for supervised and unsupervised learning:
Tools and usage
H2O Flow is an interactive web application that helps data scientists to perform various tasks from importing data to running complex models using point and click and wizard-based concepts.
H2O is run in local mode as:
java –Xmx6g –jar h2o.jarThe default way to start Flow is to point your browser and go to the following URL: http://192.168.1.7:54321/. The right-side of Flow captures every user action performed under the tab OUTLINE. The actions taken can be edited and saved as named flows for reuse and collaboration, as shown in the figure below:
The figure below shows the interface for importing files from the local filesystem or HDFS and displays detailed summary statistics as well as next actions that can be performed on the dataset. Once the data is imported, it gets a data frame reference in the H2O framework with the extension of .hex. The summary statistics are useful in understanding the characteristics of data such as missing, mean, max, min, and so on. It also has an easy way to transform the features from one type to another, for example, numeric features with a few unique values to categorical/nominal types known as enum in H2O.
The actions that can be performed on the datasets are:
- Visualize the data.
- Split the data into different sets such as training, validation, and testing.
- Build supervised and unsupervised models.
- Use the models to predict.
- Download and export the files in various formats.
Building supervised or unsupervised models in H2O is done through an interactive screen. Every modeling algorithm has its parameters classified into three sections: basic, advanced, and expert. Any parameter that supports hyper-parameter searches for tuning the model has a checkbox grid next to it, and more than one parameter value can be used.
Some basic parameters such as training_frame, validation_frame, and response_ column, are common to every supervised algorithm; others are specific to model types, such as the choice of solver for GLM, the activation function for deep learning, and so on. All such common parameters are available in the basic section. Advanced parameters are settings that afford greater flexibility and control to the modeler if the default behavior must be overridden. Several of these parameters are also common across some algorithms—two examples are the choice of method for assigning the fold index (if cross-validation was selected in the basic section), and selecting the column containing weights (if each example is weighted separately), and so on.
Expert parameters define more complex elements such as how to handle the missing values, model-specific parameters that need more than a basic understanding of the algorithms, and other esoteric variables. In the figure below, GLM, a supervised learning algorithm, is being configured with 10-fold cross-validation, binomial (two-class) classification, efficient LBFGS optimization algorithm, and stratified sampling for cross-validation split:
The model results screen contains a detailed analysis of the results using important evaluation charts, depending on the validation method that was used. At the top of the screen are possible actions that can be taken, such as to run the model on unseen data for prediction, download the model as POJO format, export the results, and so on.
Some of the charts are algorithm-specific, like the scoring history that shows how the training loss or the objective function changes over the iterations in GLM—this gives the user insight into the speed of convergence as well as into the tuning of the iterations parameter. We see the ROC curves and the Area Under Curve metric on the validation data in addition to the gains and lift charts, which give the cumulative capture rate and cumulative lift over the validation sample respectively.
The figure below shows SCORING HISTORY, ROC CURVE, and GAINS/LIFT charts for GLM on 10-fold cross-validation on the CoverType dataset:
The output of validation gives detailed evaluation measures such as accuracy, AUC, err, errors, f1 measure, MCC (Mathews Correlation Coefficient), precision, and recall for each validation fold in the case of cross-validation as well as the mean and standard deviation computed across all.
The prediction action runs the model using unseen held-out data to estimate the out-of-sample performance. Important measures such as errors, accuracy, area under curve, ROC plots, and so on, are given as the output of predictions that can be saved or exported.
H2O is a rich visualization and analysis framework that is accessible from multiple programming environments( HDFS, SQL, NoSQL, S3, and others). It can also support a number of Machine Learning algorithms that can be run in a cluster.
All these factors make it one of the major Machine Learning framework on Big Data.
If you think this post is useful, do not miss to check our book Mastering Java Machine Learning to know more on predictive models for batch- and stream-based big data learning using the latest tools and methodologies.
