H2O Machine Learning with KNIME Analytics Platform - Christian Dietz - H2O AI World London
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This talk was recorded in London on October 30, 2018. KNIME Analytics Platform is an easy to use and comprehensive open source data integration, analysis, and exploration platform, enabling data scientists to visually compose end to end data analysis workflows. The over 2,000 available modules ("nodes") cover each step of the analysis workflow, including blending heterogeneous data types, data transformation, wrangling and cleansing, advanced data visualization, or model training and deployment. Many of these nodes are provided through open source integrations (why reinvent the wheel?). This provides seamless access to large open source projects such as Keras and Tensorflow for deep learning, Apache Spark for big data processing, Python and R for scripting, and more. These integrations can be used in combination with other KNIME nodes meaning that data scientists can freely select from a vast variety of options when tackling an analysis problem. The integration of H2O in KNIME offers an extensive number of nodes and encapsulating functionalities of the H2O open source machine learning libraries, making it easy to use H2O algorithms from a KNIME workflow without touching any code - each of the H2O nodes looks and feels just like a normal KNIME node - and the data scientist benefits from the high performance libraries and proven quality of H2O during execution. For prototyping these algorithms are executed locally, however training and deployment can easily be scaled up using a Sparkling Water cluster. In our talk we give a short introduction to KNIME Analytics Platform and then demonstrate how data scientists benefit from using KNIME Analytics Platform and H2O Machine Learning in combination by using a real world analysis example. Bio: Christian received a Master’s degree in Computer Science from the University of Konstanz. Having gained experience as a research software engineer at the University of Konstanz, where he developed frameworks and libraries in the fields of bioimage analysis and machine learning, Christian moved on to become a software engineer at KNIME. He now focuses on developing new functionalities and extensions for KNIME Analytics Platform. Some of his recent projects include deep learning integrations built upon Keras and Tensorflow, extensions for image analysis and active learning, and the integration of H2O Machine Learning and H2O Sparkling Water in KNIME Analytics Platform.
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H2O Machine Learning with KNIME Analytics Platform - Christian Dietz - H2O AI World London
- 1. © 2018 KNIME AG. All rights reserved. Leveraging H2O Machine Learning with KNIME Analytics Platform Christian Dietz KNIME
- 2. H2O Distributed Machine Learning Algorithms Supervised Learning • Generalized Linear Models: Binomial, Gaussian, Gamma, Poisson and Tweedie • Naïve Bayes Statistical Analysis Ensembles • Distributed Random Forest: Classification or regression models • Gradient Boosting Machine: Produces an ensemble of decision trees with increasing refined approximations Deep Neural Networks • Deep learning: Create multi-layer feed forward neural networks starting with an input layer followed by multiple layers of nonlinear transformations Unsupervised Learning • K-means: Partitions observations into k clusters/groups of the same spatial size. Automatically detect optimal k Clustering Dimensionality Reduction • Principal Component Analysis: Linearly transforms correlated variables to independent components • Generalized Low Rank Models: extend the idea of PCA to handle arbitrary data consisting of numerical, Boolean, categorical, and missing data Anomaly Detection • Autoencoders: Find outliers using a nonlinear dimensionality reduction using deep learning
- 3. Platforms with H2O Integration H2O + KNIME Talk at KNIME Summit March 2018
- 4. © 2018 KNIME AG. All rights reserved. 4 KNIME® • KNIME AG founded in 2008 • Offices in Zurich (HQ), Konstanz, Berlin, and Austin • Maintainer of the Open Source KNIME Analytics Platform – comprehensive data loading, processing, analysis, modeling platform – visual frontend – open: to all sorts of data, other tools (R and Python, etc.), various user personas – 20+ open source releases since 2006 – open source. • KNIME Server – 14 commercial product releases since 2008 • KNIME cloud offerings
- 5. © 2018 KNIME AG. All rights reserved. 5 KNIME® Software
- 6. © 2018 KNIME AG. All rights reserved. 6 KNIME® Analytics Platform
- 7. © 2018 KNIME AG. All rights reserved. 7 Analysis & Mining Statistics Data Mining Machine Learning Deep Learning Web Analytics Text Mining Network Analysis Social Media Analysis R, Weka, Python, H2O Community / 3rd Data Access MySQL, Oracle, ... SAS, SPSS, ... Excel, Flat, ... Hive, Impala, ... XML, JSON, PMML Text, Doc, Image, ... Web Crawlers Industry Specific Community / 3rd Transformation Row, Column Matrix Text, Image Time Series Java Python Community / 3rd Visualization R Python JavaScript Community / 3rd Deployment via BIRT PMML XML, JSON Databases Excel, Flat, etc. Text, Doc, Image Industry Specific Community / 3rd Over 2000 Native and Embedded Nodes Included
- 8. © 2018 KNIME AG. All rights reserved. 8 KNIME H2O Machine Learning Integration • Offer our users high-performance machine learning algorithms from H2O in KNIME • Allow to mix & match with other KNIME functionality – Data wrangling KNIME Analytics Platform functionality – KNIME Big-Data Connectors – Text Mining, Image Processing, Cheminformatics, … – and more!
- 9. © 2018 KNIME AG. All rights reserved. 9 KNIME H2O Machine Learning Integration
- 10. © 2018 KNIME AG. All rights reserved. 10 The Data Date Store ID Visitors 2016-01-01 ba937bf13d40fb24 28 … … … 2017-04-22 324f7c39a8410e7c 216 Date Store ID Visitors 2017-04-23 e8ed9335d0c38333 ? … … … 2017-05-31 8f13ef0f5e8c64dd ? Provided data: • Number of visitors • Reservations • Store information • Calendar date info
- 11. © 2018 KNIME AG. All rights reserved. 11 Visitor Forecasting Data preparation Model training Model optimization Model evaluation Deployment
- 12. © 2018 KNIME AG. All rights reserved. 12 Visitor Forecasting Data preparation Model training Model optimization Model evaluation Deployment
- 13. © 2018 KNIME AG. All rights reserved. 13 Data Preparation with KNIME Nodes
- 14. © 2018 KNIME AG. All rights reserved. 15 Visitor Forecasting Data preparation Model training Model optimization Model evaluation Deployment
- 15. © 2018 KNIME AG. All rights reserved. 16 Visitor Forecasting Data preparation Model training Model optimization Model evaluation Deployment
- 16. © 2018 KNIME AG. All rights reserved. 17 Modeling with the H2O Nodes
- 17. © 2018 KNIME AG. All rights reserved. 18 Modeling with the H2O Nodes
- 18. © 2018 KNIME AG. All rights reserved. 19 Modeling with the H2O Nodes
- 19. © 2018 KNIME AG. All rights reserved. 20 Modeling with the H2O Nodes
- 20. © 2018 KNIME AG. All rights reserved. 21 Visitor Forecasting Data preparation Model training Model optimization Model evaluation Deployment
- 21. © 2018 KNIME AG. All rights reserved. 22 Visitor Forecasting . Data preparation Model training Model optimization Model evaluation Deployment
- 22. © 2018 KNIME AG. All rights reserved. 24 Blend H2O with…Python, Java and R Scripting… 24
- 23. © 2018 KNIME AG. All rights reserved. 25 …Image Processing... 25
- 24. © 2018 KNIME AG. All rights reserved. 26 …Deep Learning...
- 25. © 2018 KNIME AG. All rights reserved. 27 ...Text Processing...
- 26. © 2018 KNIME AG. All rights reserved. 28 ...Databases...
- 27. © 2018 KNIME AG. All rights reserved. 29 ...a growing Big Data Integration.
- 28. © 2018 KNIME AG. All rights reserved. 30 H2O Sparkling Water in KNIME
- 29. © 2018 KNIME AG. All rights reserved. 31 H2O Sparkling Water in KNIME
- 30. © 2018 KNIME AG. All rights reserved. 32 H2O Sparkling Water in KNIME
- 31. © 2018 KNIME AG. All rights reserved. 33 H2O Sparkling Water in KNIME
- 32. © 2018 KNIME AG. All rights reserved. 34 H2O Sparkling Water in KNIME
- 33. © 2018 KNIME AG. All rights reserved. 35 Scoring with H2O MOJOs on Apache Spark
- 34. © 2018 KNIME AG. All rights reserved. 36 Thank You! www.knime.com
- 35. 37© 2018 KNIME AG. All rights reserved. The KNIME® trademark and logo and OPEN FOR INNOVATION® trademark are used by KNIME AG under license from KNIME GmbH, and are registered in the United States. KNIME® is also registered in Germany.
Editor's Notes
- this competition was published by a japanese Restaurant chain. They wanted to know the number of future visitors for their different stores Lets see what kind of data they provided us to solve this problem
- This is the top-level-workflow we used to solve the problem It will guide us through the major steps from reading in the data up to doing the prediction and showcases the interaction of the knime native and the h2o nodes
- Lets jump right into our data preparation
- Data preparation part of the workflow We‘ll not discuss it in too much detail
- In the end we get two datasets the trainset with information about the number of visitors (target variable), which we will use to build our model in the next steps the test dataset without the number of visitors. These have to be predicted by our model and submitted to kaggle lateron
- We just did the data preparation, before we jump right into the modeling we have to create a local h2o context and convert our knime table into an h2o frame This frame will be used to build our models
- At the moment there are three H2O models implemented in KNIME which are capable of solving such a regression task: Random Forest, Generalized Linear Model and Gradient Boosting Machine Lets have a look at one of those to see how we trained, optimized and evaluated our models
- The actual learning of a model happens in one single node: The H2O Random Forest Learner takes the h2o frame with the testset and builds a model Configuration dialog: What is the target variable you want to predict? Here it is visitors, enter some model specific parameters, e.g. number of levels of a single tree and the number of tree models in this forest Next we use the H2O predictor to use the just created model to predict the visitors for our testset Afterwards the score of the model is computed with the H2O regression scorer. as performance measure we used the root mean squared logarithmic error, as this measure is also used on Kaggle to evaluate the final submissions.
- To avoid overfitting we use the h2o cross validation loop, which partitions the data and trains one model for each partition of the data !!! Tabelle mit mean von cv einbauen !!!
- With one machine learning algorithm, here e.g. random forest, you can solve different problems. With parameters, for a random forest e.g. the number of trees and the treedepth, one can adapt it to a specific problem with respect to the objective function. Here we are looking for parameters that minimize the error of our model validations We did it with a grid search that performs one iteration of the loop for every possible combination of parameters At the end we have a table with all parameter combinations and their respective scores
- At the end of the loop we’ve got all parameter combinations with their respective scores. We selected the parameters that lead to the best result and trained a new model on the complete public dataset As you can see we’ve got a nested loop here. Luckily the new H2O nodes are really fast, so this is not gonna be a performance issue
- The steps I just showed you happen in all three nodes. Afterwards we select the model which scored best
- convert it into an h2o MOJO, which is a model object that is optimized to be embedded in any java environment By doing this we are able to use our just created model outside of an H2O context. We can for example do our prediction for the submission dataset from Kaggle Or we can deploy it to where ever we want, so we just stored it somewhere for Christian. Lets see he is doing with it.