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Using H2O Random Grid Search for Hyper-parameters Optimization

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Jo-fai Chow
Jo-fai Chow

My talk at PyData Amsterdam

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Using H2O Random Grid Search for Hyper-parameters Optimization Jo-fai (Joe) Chow Data Scientist joe@h2o.ai
WHO AM I • Customer Data Scientist at H2O.ai • Background o Telecom (Virgin Media) o Data Science Platform (Domino Data Lab) o Water Engineering + Machine Learning Research (STREAM Industrial Doctorate Centre)
ABOUT H2O • Company o Team: 50 (45 shown) o Founded in 2012, Mountain View, California. o Venture capital backed • Products o Open-source machine learning platform. o Flow (Web), R, Python, Spark, Hadoop interfaces.
ABOUT THIS TALK • Story of a baker and a data scientist o Why you should care • Hyper-parameters optimization o Common techniques o H2O Python API • Other H2O features for streamlining workflow
STORY OF A BAKER • Making a cake o Source • Ingredients o Process: • Mixing • Baking • Decorating o End product • A nice looking cake Credit: www.dphotographer.co.uk/image/201305/baking_a_cake
STORY OF A DATA SCIENTIST • Making a data product o Source • Raw data o Process: • Data munging • Analyzing / Modeling • Reporting o End product • Apps, graphs or reports Credit: www.simranjindal.com
BAKER AND DATA SCIENTIST • What do they have in common? o Process is important to bakers and data scientists. Yet, most customers do not appreciate the effort. o Most customers only care about raw materials quality and end products.
WHY YOU SHOULD CARE • We can use machine/software to automate some laborious tasks. • We can spend more time on quality assurance and presentation. • This talk is about making one specific task, hyper-parameters tuning, more efficient.
HYPER-PARAMETERS OPTIMISATION • Overview o Optimizing an algorithm’s performance. • e.g. Random Forest, Gradient Boosting Machine (GBM) o Trying different sets of hyper-parameters within a defined search space. o No rules of thumb.
HYPER-PARAMETERS OPTIMISATION • Example of hyper-parameters in H2O o Random Forest: • No. of trees, depth of trees, sample rate … o Gradient Boosting Machine (GBM): • No. of trees, depth of trees, learning rate, sample rate … o Deep Learning: • Activation, hidden layer sizes, L1, L2, dropout ratios …
COMMON TECHNIQUES • Manual search o Tuning by hand - inefficient o Expert opinion (not always reliable) • Grid search o Automated search within a defined space o Computationally expensive • Random grid search o More efficient than manual / grid search o Equal performance in less time
RANDOM GRID SEARCH – DOES IT WORK? • Random Search for Hyper-Parameter Optimization o Journal of Machine Learning Research (2012) o James Bergstra and Yoshua Bengio o “Compared with deep belief networks configured by a thoughtful combination of manual search and grid search, purely random search found statistically equal performance on four of seven data sets, and superior performance on one of seven.”
RELATED FEATURE – EARLY STOPPING • A technique for regularization. • Avoid over-fitting the training set. • Useful when combined with hyper-parameter search: o Additional controls (e.g. time constraint, tolerance)
H2O RANDOM GRID SEARCH • Objectives o Optimize model performance based on evaluation metric. o Explore the defined search space randomly. o Use early-stopping for regularization and additional controls.
RANDOM GRID SEARCH (PYTHON API)
RANDOM GRID SEARCH • Outputs o Best model based on metric o A set of hyper-parameters for the best model • Other APIs o R, REST, Java (see documentation on GitHub)
OTHER H2O FEATURES • h2oEnsemble o Better predictive performance • Sparkling Water = Spark + H2O • Plain Old Java Object (POJO) o Productionize H2O models
CONCLUSIONS • Most people only care about the end product. • Use H2O random grid search to save time on hyper-parameters tuning. • Spend more time on quality assurance and presentation.
CONCLUSIONS • H2O Random Grid Search o An efficient way to tune hyper-parameters o APIs for Python, R, Java, REST o Do check out the code examples on GitHub • Combine with other H2O features o Streamline data science workflow
ACKNOWLEDGEMENTS • GoDataDriven • Conference sponsors • My colleagues at H2O.ai
THANK YOU • Resources o Slides + code – github.com/h2oai/h2o-meetups o Download H2O – www.h2o.ai o Documentation – www.h2o.ai/docs/ o joe@h2o.ai • We are hiring – www.h2o.ai/careers/

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Using H2O Random Grid Search for Hyper-parameters Optimization

  • 1. Using H2O Random Grid Search for Hyper-parameters Optimization Jo-fai (Joe) Chow Data Scientist joe@h2o.ai
  • 2. WHO AM I • Customer Data Scientist at H2O.ai • Background o Telecom (Virgin Media) o Data Science Platform (Domino Data Lab) o Water Engineering + Machine Learning Research (STREAM Industrial Doctorate Centre)
  • 3. ABOUT H2O • Company o Team: 50 (45 shown) o Founded in 2012, Mountain View, California. o Venture capital backed • Products o Open-source machine learning platform. o Flow (Web), R, Python, Spark, Hadoop interfaces.
  • 4. ABOUT THIS TALK • Story of a baker and a data scientist o Why you should care • Hyper-parameters optimization o Common techniques o H2O Python API • Other H2O features for streamlining workflow
  • 5. STORY OF A BAKER • Making a cake o Source • Ingredients o Process: • Mixing • Baking • Decorating o End product • A nice looking cake Credit: www.dphotographer.co.uk/image/201305/baking_a_cake
  • 6. STORY OF A DATA SCIENTIST • Making a data product o Source • Raw data o Process: • Data munging • Analyzing / Modeling • Reporting o End product • Apps, graphs or reports Credit: www.simranjindal.com
  • 7. BAKER AND DATA SCIENTIST • What do they have in common? o Process is important to bakers and data scientists. Yet, most customers do not appreciate the effort. o Most customers only care about raw materials quality and end products.
  • 8. WHY YOU SHOULD CARE • We can use machine/software to automate some laborious tasks. • We can spend more time on quality assurance and presentation. • This talk is about making one specific task, hyper-parameters tuning, more efficient.
  • 9. HYPER-PARAMETERS OPTIMISATION • Overview o Optimizing an algorithm’s performance. • e.g. Random Forest, Gradient Boosting Machine (GBM) o Trying different sets of hyper-parameters within a defined search space. o No rules of thumb.
  • 10. HYPER-PARAMETERS OPTIMISATION • Example of hyper-parameters in H2O o Random Forest: • No. of trees, depth of trees, sample rate … o Gradient Boosting Machine (GBM): • No. of trees, depth of trees, learning rate, sample rate … o Deep Learning: • Activation, hidden layer sizes, L1, L2, dropout ratios …
  • 11. COMMON TECHNIQUES • Manual search o Tuning by hand - inefficient o Expert opinion (not always reliable) • Grid search o Automated search within a defined space o Computationally expensive • Random grid search o More efficient than manual / grid search o Equal performance in less time
  • 12. RANDOM GRID SEARCH – DOES IT WORK? • Random Search for Hyper-Parameter Optimization o Journal of Machine Learning Research (2012) o James Bergstra and Yoshua Bengio o “Compared with deep belief networks configured by a thoughtful combination of manual search and grid search, purely random search found statistically equal performance on four of seven data sets, and superior performance on one of seven.”
  • 13. RELATED FEATURE – EARLY STOPPING • A technique for regularization. • Avoid over-fitting the training set. • Useful when combined with hyper-parameter search: o Additional controls (e.g. time constraint, tolerance)
  • 14. H2O RANDOM GRID SEARCH • Objectives o Optimize model performance based on evaluation metric. o Explore the defined search space randomly. o Use early-stopping for regularization and additional controls.
  • 15. RANDOM GRID SEARCH (PYTHON API)
  • 16. RANDOM GRID SEARCH • Outputs o Best model based on metric o A set of hyper-parameters for the best model • Other APIs o R, REST, Java (see documentation on GitHub)
  • 17. OTHER H2O FEATURES • h2oEnsemble o Better predictive performance • Sparkling Water = Spark + H2O • Plain Old Java Object (POJO) o Productionize H2O models
  • 18. CONCLUSIONS • Most people only care about the end product. • Use H2O random grid search to save time on hyper-parameters tuning. • Spend more time on quality assurance and presentation.
  • 19. CONCLUSIONS • H2O Random Grid Search o An efficient way to tune hyper-parameters o APIs for Python, R, Java, REST o Do check out the code examples on GitHub • Combine with other H2O features o Streamline data science workflow
  • 20. ACKNOWLEDGEMENTS • GoDataDriven • Conference sponsors • My colleagues at H2O.ai
  • 21. THANK YOU • Resources o Slides + code – github.com/h2oai/h2o-meetups o Download H2O – www.h2o.ai o Documentation – www.h2o.ai/docs/ o joe@h2o.ai • We are hiring – www.h2o.ai/careers/

Notas del editor

  1. New face in PyData conference Thanks for having me I am Joe. I work for H2O. Only joined 6 weeks ago Not really a contributor to their code base – more like a long-term user (using H2O for my previous jobs since 2014) Today’s talk is about one specific feature in H2O Instead of giving you a full demo Tell you a story from a user’s perspective
  2. A little bit more background of me H2O – helping customers leverage H2O with their data, build smart apps Before I joined H2O VM – building data apps for CEO and CFO – answering specific business questions Domino – evangelist – blogging and going to meetups and conferences Before data science journey, I was a civil engineer – working on water related research
  3. Team is growing rapidly Based in Mountain View – I am the only guy in UK/Europe VC: $34M in total
  4. Start with a story of baker / data scientist Background information Explain why you should care about the main topic – hyper-para tuning What is it Techniques API Other useful features in H2O
  5. Start with a story Or a workflow of making a cake
  6. Raw data – structured / unstructured / different databases
  7. Customers don’t see much of the process