![22
The Bank Marketing Dataset
• Background
– A Portuguese banking institution runs a marketing campaign for selling long-term deposits and
collects data from clients they contact, covering various socioeconomic indicators.
– These data are from the years 2008-2013
– The predicted variable is whether or not the client subscribed to the service.
• 45,211 observations
• 17 input features of mixed numerical and categorical data
• Contact communication type (‘email’ vs.
‘phone’)
• Client education level
• Age
• Contact day-of-week
• Employment
• Has loans
• Number of days since last contact
• Contact Month
• Previous contact outcome
• Consumer Price Index
• Duration of Contact
• Others
Source: http://mlr.cs.umass.edu/ml/datasets/Bank+Marketing
[Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to
Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier,
62:22-31, June 2014](https://image.slidesharecdn.com/2015-june-bigdata-la-150722205755-lva1-app6891/85/Big-Data-Day-LA-2015-Scalable-and-High-Performance-Analytics-with-Distributed-R-and-Vertica-by-Edward-Ma-of-HP-22-320.jpg)
![29
Computing the Z-Score
1) To normalize the data, we will compute the z-score for all quantitative variables.
𝒛 =
𝒙 − 𝝁
𝝈
1) For these features, we’ll first have to compute the mean and standard deviation.
m_sd <- summarise(orig,m_age=mean(age),std_age=sd(age) ….
summarise in dplyr collapses columns into aggregates.
3) Then we’ll need to convert the quantitative values into z_scores for every
observation
normalized <- mutate(orig,age_z=(age-z[["m_age"]])/z[["std_age"]] …..
mutate creates new columns – in this case, we are creating new columns to store](https://image.slidesharecdn.com/2015-june-bigdata-la-150722205755-lva1-app6891/85/Big-Data-Day-LA-2015-Scalable-and-High-Performance-Analytics-with-Distributed-R-and-Vertica-by-Edward-Ma-of-HP-29-320.jpg)
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Big Data Day LA 2015 - Scalable and High-Performance Analytics with Distributed R and Vertica by Edward Ma of HP
The document presents an overview of scalable and high-performance analytics using Distributed R and Vertica within the context of predictive analytics applications across various industries. It details the architecture, implementation, and operational workflow of Distributed R, emphasizing its improvements in data processing and model deployment capabilities. The document also highlights integration with Vertica for enhanced data analytics and in-database predictions, showcasing an end-to-end predictive analytics demo with a real dataset.
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Big Data Day LA 2015 - Scalable and High-Performance Analytics with Distributed R and Vertica by Edward Ma of HP
- 1. SCALABLE AND HIGH-PERFORMANCE ANALYTICSWITH DISTRIBUTED R AND VERTICA Big Data Day 2015 Los Angeles, CA Edward Ma June 27th, 2015
- 2. 2 Predictive analytics applications Marketing Sales Logistics Risk Customersupport Human resources … Healthcare Consumer financial Retail Insurance Life sciences Travel …
- 3. 3 Haven Big Data Platform Turn100% of your data into action. Human Data Business Data Machine Data Powering Big Data Analytics to Applications Insight Haven OnDemand • Vertica OnDemand • IDOL OnDemand • Vertica Enterprise • IDOL Enterprise • Vertica for SQL on Hadoop • Vertica Distributed R • KeyView Haven Enterprise HP Haven Big Data Platform
- 4. 4 Predictive analytics workflow BuildModels Evaluate Models Deploy Models (In-DB or Web) BI Integration 1 2 3 Build and evaluate predictive models on large datasets using Distributed R 2 1 Ingest and prepare data by leveraging HP Vertica Analytics Platform (SQL DB) 3 Deploy models to Vertica and use in-database scoring to produce prediction results for BI and applications. Alternatively deploy model as a web service.
- 5. 5 Outline • Distributed ROverview and Examples • Full-cycle, “end-to-end” predictive analytics demo with a real dataset, showcasing: • Distributed R, using the HPdglm package (Distributed R’s parallel, high-performance linear regression algorithm by HP) • Vertica, with an in-database prediction function • In-database data preparation with Vertica.dplyr
- 6. Distributed R The NextGeneration Platform for Predictive Analytics
- 7. 7 R is …. Popular Not scalable Open sourceNo parallel algorithms Flexible Extensible Limited pre/post processing “The best thing about R is that it was developed by statisticians. The worst thing about R is that… it was developed by statisticians.” -Bo Cogwill, Google
- 8. 8 Data Scientists PreferredLanguages: R & SQL Adoption of R increased across industries 1) http://www.kdnuggets.com/2014/08/four-main-languages-analytics-data-mining-data-science.html 2) http://blog.revolutionanalytics.com/2013/10/r-usage-skyrocketing-rexer-poll.html
- 9. 9 Distributed R ANew Enterpriseclasspredictive analytics platform A scalable, high-performance platform for the R language • Implemented as an R package • Open source Use familiar GUIs and packages Analyze data too large for vanilla R Leverage multiple nodes for distributed processing Vastly improved performance
- 10. 10 Distributed R: architecture Master •Schedules tasks across the cluster. • Sends commands/code to workers Workers • Hold data partitions • Apply functions to data partitions in parallel
- 11. 11 • Relies onuser defined partitioning • Also support for distributed data-frames and lists darray Distributed R: Distributed data structures
- 12. 12 • Express computationsover partitions • Execute across the cluster foreach Distributed R: Distributed code f (x)
- 13. 13 Distributed R: basicconcepts # Loads the package into R library(distributedR) # Starts up your cluster (as defined in XML) distributedR_start() # Declares a distributed array of dimensions 4x4, each partition 2x2 B <- darray(dim=c(4,4), blocks=c(2,2), sparse=FALSE) # Sets each partition to a matrix containing integers == their partition ids foreach(i, 1:npartitions(B), init<-function(b = splits(B,i), index=i) { b <- matrix(index, nrow=nrow(b), ncol=ncol(b)) update(b) })
- 14. 14 • Similar signature,accuracy as R packages • Scalable and high performance • E.g., regression on billions of rows in a couple of minutes Distributed R: Built-in distributed algorithms Algorithm Use cases Linear Regression (GLM) Risk Analysis, Trend Analysis, etc. Logistic Regression (GLM) Customer Response modeling, Healthcare analytics (Disease analysis) Random Forest Customer churn, Market campaign analysis K-Means Clustering Customer segmentation, Fraud detection, Anomaly detection Page Rank Identify influencers
- 15. 15 Distributed R: summary •Regression on billions of rows in minutes • Graph algorithms on 10B edges • Load 400GB+ data from database to R in < 10 minutes • Open source!
- 16. 16 That’s cool… whatcan I do with it? • Collaborate • Github (report issues, send PRs) https://github.com/vertica/DistributedR • Standardization with R-core http://www.r-bloggers.com/enhancing-r-for-distributed-computing/ • Get the SW + docs: http://www.vertica.com/hp-vertica-products/hp-vertica-distributed-r/ • Buy commercial support
- 17.
- 18. 18 In our Demo… BuildModels Evaluate Models Deploy Models (In-DB or Web) BI Integration 1 2 3 1 Retrieve the “bank-additional” dataset from the UCI ML repository Create a table in HP Vertica, and import the CSV of data into Vertica using the data loader. 2 3 Prepare the data using Vertica.dplyr: cleaning it up, creating new columns, and separating them into training and testing sets. 4 Load the data into Distributed R and apply the GLM algorithm on it to produce a model. 5 Deploy the model back to Vertica. 6 Apply the model to data in the testing set; check for accuracy.
- 19. 19 From Storage toTraining to Prediction Distributed R P 1 P 2 P 3 P 4 Vertica DBModel Data Vertica.dplyr
- 20. 20 • Developed fromMIT’s C-Store • Fast, columnar-oriented analytics database • Organizes data into projections • Provides k-safety fault-tolerance and redundancy • Used in several industry applications for big-data storage and analysis (see our public customer list for examples) What’s Vertica?
- 21. 21 Build Models Evaluate Models Deploy Models (In-DBor Web) BI Integration 1 2 3 1 Step 1) Retrieve the “bank-additional” dataset from the UCI ML repository
- 22. 22 The Bank MarketingDataset • Background – A Portuguese banking institution runs a marketing campaign for selling long-term deposits and collects data from clients they contact, covering various socioeconomic indicators. – These data are from the years 2008-2013 – The predicted variable is whether or not the client subscribed to the service. • 45,211 observations • 17 input features of mixed numerical and categorical data • Contact communication type (‘email’ vs. ‘phone’) • Client education level • Age • Contact day-of-week • Employment • Has loans • Number of days since last contact • Contact Month • Previous contact outcome • Consumer Price Index • Duration of Contact • Others Source: http://mlr.cs.umass.edu/ml/datasets/Bank+Marketing [Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014
- 23. 23 Build Models Evaluate Models Deploy Models (In-DBor Web) BI Integration 1 2 3 1 Step 2) Create a table in HP Vertica, and import the CSV of data into Vertica using the data loader.
- 24. 24 Vertica.dplyr: A Verticaadapter for dplyr • Dplyr is an R package that is quickly rising in popularity due to its convenient syntax and mechanisms for data manipulation in R. • filter() (and slice()), arrange(), select() (and rename()), distinct(), mutate() (and transmute()), summarise(), sample_n() and sample_frac() • dplyr supports SQL translation for many of these operations on databases, but requires specific drivers for different databases • These operations can be well leveraged for data preparation • Vertica.dplyr is not only a driver for dplyr to work with Vertica, but also a means for us to more fully integrate R with Vertica and keep R users at ease with inDB data preparation (no SQL knowledge required!) More info: http://cran.rstudio.com/web/packages/dplyr/vignettes/introduction.html
- 25. 25 Creating the tableand loading the data db_create_table(vertica$con,"bank_orig",col umns) orig <- db_load_from_file(vertica, file.name= "/home/dbadmin/bank-additional/bank- additional-full.csv", "bank_orig", sep = ";") CREATE TABLE IF NOT EXISTS bank_original (age int, job varchar, marital varchar, education varchar, "default" varchar, housing varchar, loan varchar, contact varchar, MONTH varchar, day_of_week char(5), duration int, campaign int, pdays int, "previous" int, poutcome varchar, "emp.var.rate" float, "cons.price.idx" float, "cons.conf.idx" float, euribor3m float, "nr.employed" float, y varchar(5)); COPY bank_original FROM '/home/dbadmin/bank-additional/bank- additional-full.csv' Vertica.dplyr SQL
- 26. 26 Build Models Evaluate Models Deploy Models (In-DBor Web) BI Integration 1 2 3 1 Step 3) Prepare the data using Vertica.dplyr
- 27. 27 Data Preparation Steps 1)Normalize some columns 2) Relabel low-frequency categorical data 3) Change categorical data to numerical data 4) Separate data into training and testing sets
- 28. 28 Data Preparation Steps 1)Normalize some columns 2) Relabel low-frequency categorical data 3) Change categorical data to numerical data 4) Separate data into training and testing sets
- 29. 29 Computing the Z-Score 1)To normalize the data, we will compute the z-score for all quantitative variables. 𝒛 = 𝒙 − 𝝁 𝝈 1) For these features, we’ll first have to compute the mean and standard deviation. m_sd <- summarise(orig,m_age=mean(age),std_age=sd(age) …. summarise in dplyr collapses columns into aggregates. 3) Then we’ll need to convert the quantitative values into z_scores for every observation normalized <- mutate(orig,age_z=(age-z[["m_age"]])/z[["std_age"]] ….. mutate creates new columns – in this case, we are creating new columns to store
- 30. 30 Data Preparation Steps 1)Normalize some columns 2) Relabel low-frequency categorical data 3) Change categorical data to numerical data 4) Separate data into training and testing sets
- 31. 31 Let’s take alook at the categorical variables 1) Group and sort job_group <- group_by(norm_table,job) arrange(summarise(job_group,freq=n()),desc(freq)) SQL equivalent: SELECT job, COUNT(*) AS freq FROM bank_normalized GROUP BY job ORDER BY freq DESC; 2) Many categories have much higher frequencies than others. Let’s use the DECODE function to relabel the low-frequency occurrences: decode(job,'"admin"',"admin",'"blue-collar"',"blue- collar",'"technician"',"technician",'"services"',"services",'"management"',"management","other ")
- 32. 32 A B CD E F Before A B C Other After Reclassifying Low-Frequency Categorical Occurrences
- 33. 33 Data Preparation Steps 1)Normalize some columns 2) Relabel low-frequency categorical data 3) Change categorical data to numerical data 4) Separate data into training and testing sets
- 34. 34 cat2num Why? HPdglm() requires categoriesto be relabeled to numbers; i.e. classes must be changed to numbers. cat2num("bank_top_n",dsn="VerticaDSN ",dstTable="bank_top_n_num") 3 Pet Type Number ‘Cat’ 0 ‘Dog’ 1 ‘Fish’ 2 ‘Snake’ 3
- 35. 35 Data Preparation Steps 1)Normalize some columns 2) Relabel low-frequency categorical data 3) Change categorical data to numerical data 4) Separate data into training and testing sets
- 36. 36 Separating data intotesting and training sets top_tbl <- tbl(vertica,"bank_top_n_num") testing_set <- filter(top_tbl,random() < 0.2) testing_set <- compute(testing_set,name="testing_set") training_set <- compute(setdiff(top_tbl,testing_set),"training_set") 3
- 37. © Copyright 2014Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. Training predictive models
- 38. 38 Build Models Evaluate Models Deploy Models (In-DBor Web) BI Integration 1 2 3 1 Step 4) Load data into Distributed R and Train the Model
- 39. Let’s load thedata into Distributed R! …but how?
- 40. 40 Approach 1: Toomany connections Parallel loading from DBR = Multiple concurrent ODBC connections Each connection requests part of the same database table • 10 servers * 32 HT cores = 320 concurrent SQL queries • Costly intra-node transfers in DB Overwhelms the database! Database Worke r Worke r Worke r Distributed R
- 41. 41 Solution: Reduce connections,DB pushes data Master R process requests table : single SQL request • Provides hint about number of partitions Vertica starts multiple UDFs • Reads table from DB, divides data into partitions • Sends data in parallel to Distributed R nodes (over network) Distributed R workers receive data • Converts data to in-memory R objects Prasad, Shreya; Fard, Arash; Gupta, Vishrut; Martinez, Jorge; LeFevre, Jeff; Xu, Vincent; Hsu, Meichun; Roy, Indrajit (2015). "Enabling predictive analytics in Vertica: Fast data transfer, distributed model creation and in-database prediction". ACM SIGMOD International Conference on Management of Data (SIGMOD). Database Maste r Worke r Worke r SQL data Distributed R
- 42. 42 Package HPdata Includes manyfunctions for loading data into Distributed R, including from Vertica as well as the file system, giving you functions like: 1) db2darrays 2) db2dframe 3) db2matrix 4) file2dgraph 5) etc. The DB functions make full advantage of the Vertica Fast Transfer feature.
- 43. 43 Build Models Evaluate Models Deploy Models (In-DBor Web) BI Integration 1 2 3 1 Step 5) Deploy the Model to Vertica and Evaluate
- 44. 44 OK, I’ve gotthe model, but now how do I do my in-database prediction? Step 1: Deploy the Model deploy.model(model=theModel, dsn='SF', modelName='demoModel', modelComments='A logistic regression model for bank data‘) This converts the R model into a table in Vertica, where the parameters can be used to predict on new data. Distributed R P 1 P 2 P 3 P 4 Vertica DBModel
- 45. 45 OK, I’ve gotthe model, but now how do I do my in-database prediction? Step 2: Run the prediction function, GLMpredict() The Distributed R Extensions for HP Vertica pack contains a set of Vertica functions that increase the synergy between R and Vertica, with prediction functions for R models generated by: • hpdglm/glm • hpdkmeans/kmeans • hpdrandomForest/randomForest
- 46. © Copyright 2014Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. Conclusions
- 47. 47 • Distributed R •Scalable, high-performance analytics for big data • Compatibility with R’s massive package-base at the executor level • Open source • Predictive analytics tool complementing Vertica in the HP Haven Platform • Vertica.dplyr • Leverages power of dplyr for Vertica • Helps keep data sandboxing in R • Integration with Distributed R Summary
- 48.