Machine Learning at Scale
using h2o
Giri Tatavarty
Data Science Manager – R&D
dunnhumby inc
https://www.linkedin.com/in/giridhar-tatavarty-1a16503
What is Machine Learning ?
● It is field of computer science that has concepts from statistics,
pattern recognition, artificial intelligence and computational
learning theory. It is about algorithms which can teach themselves
to recognize patterns in data without programming explicitly.
● Supervised learning: The algorithm is presented with example
inputs and their desired outputs, and the goal is to learn a general
rule that maps inputs to outputs. Example: Image tagging,voice
recognition, fraud detection, time series forecasting, predictive analytics,
spam detection, face recognition, finger print recognition, handwriting
recognition, Netflix or Amazon recommendations
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What is Machine Learning?
● Unsupervised learning: No labels/examples are given to the
learning algorithm, leaving algorithm n its own to find structure in
the data. Example: Clustering, Segmentations
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What is Machine Learning?
● Reinforcement learning: A computer program interacts with a
dynamic environment in which it must perform a certain goal (such
as driving a vehicle, or playing chess game), without a teacher
explicitly telling it whether it has come close to its goal.
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Machine Learning in R
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Machine Learning with R
● Different packages for different ML algorithms
– xgboost,rpart,randomforest,bigrf,glmnet,gbm
● Programmatic Interface and parameters inputs are very different
for these packages
● Data needs to be standardized
– Convert categorical text data to multiple binary columns
– Standardize values/ Scale them
● Hyper parameter optimization framework missing with standard
packages
● Working with BigData as these packages are limited by the size
of single working machine
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h2o solves some of these problems
● Standard interface for all different algorithms
● No need to standardize or covert categorical variables
● Runs on big data . Same code can run on your laptop as well as
100 node cluster.
● Generates production ready high performance java code for
scoring
● Can work with other languages such as python / REST api or use
the web interface for data exploration and analysis
Cons
● Limited by the implementation of h2o ( to be fair h2o is
opensource)
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H2o architecture
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H2o architecture -II
Data is compressed /
chunked and distributed
across the nodes
Processing is done in a
tree based topology to
minimize inter node
communication and
summarize the data locally
as much as possible.
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H2o Installation in R
# install latest version from Cron or specific version from Amazon
install.packages("h2o", type="source", repos=(c("http://h2orelease.s3.amazonaws.com/h2o/rel-tibshirani/8/R")))
or
install.packages("h2o")
# load the library and start up a local h2o engine
library(h2o)
localH2O = h2o.init(nthreads=-1)
# Run the demo
demo(h2o.kmeans)
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Baby Steps - Iris dataset
Iris data set
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–
150 examples
–
4 attributes
–
4 classes
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Task : Train the model to predict species (class) of the flower based on attributes
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Model Statistics – Open browser http://localhost:54321 ( Free Plots)
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Step 2: Split data into training and test datasets; Report performance on unseen data
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Big Data Test Case – http://www.dunnhumby.com/sourcefiles.aspx
~ 50 GB
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~ 300M
rows
22
columns
DataSet 1 - Transactions
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PREDICT IF CUSTOMER IS GOING TO VISIT THE
STORE NEXT WEEK,
BASED ON PREVIOUS VISITS
If yes then predict what he is likely to buy and activate the promotion
channels necessary.
How do we go about predictions
● Create a model which takes your spend on previous 12 weeks ( or
n weeks ) to predict the current week visit
– Create a Training Data Set
– Create a Test Data Set
– Train the Model on Training data set
– Test the predictions on Test Dataset
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Large dataset ingestion on laptop
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A bit of data munging
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Plots from R using h2o.hist
h2o.hist(subset.hex$SPEND[subset.hex$SPEND<30 ])
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Summarize Data and create features - h2o.group_by
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Final Dataset before ML Model
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Creating a Random Forest Model
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Using Flow to explore the Model
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Change and explore thresholds
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Logistic Regression – h2o.glm
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Gradient Boost Machines
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Deep Learning and Neural Networks
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Machine Learning Algorithms supported
● K-Means
● GLM (generalized linear models)
● DRF ( Distributed Random forest)
● Naïve Bayes
● PCA (Principal Component Analysis)
● GBM (Gradient Boosting)
● Deep Learning
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Meta Learning
● Grid Search & non-negative least squares (NNLS)
● Ensemble Models
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Resources
● http://h2o.ai
● http://bit.ly/1Qh79Xr h2o booklet on R
● http://www.dunnhumby.com/sites/default/files/filepicker/1/dunnhu
mby_-_Let_s_Get_Sort-of-Real_User_Guide.pdf
●
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