Big Data Science in Scala

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This talk presents you how three scala libraries - Smile, Saddle and Spark ML - satisfy requirements of new Big Data Science projects. Let's see it on example of click-through rate prediction.

Data & Analytics

Big Data Science
in Scala
Anastasia Lieva
Data Scientist
@lievAnastazia

1. R
2. Python
3. SQL
2014
KDnuggets Polls: most popular tools in data-science
2015
2016

Context: Real Time Bidding
Raw requests: 100 000 requests per second
4 terabytes per day

R
Python
SQL
Scala
Spark
ML/DATAFRAME/SQL
SMILE
Saddle

Spark Saddle Smile
Preprocessing
Machine Learning
Evaluation
Preprocessing
Machine
Learning
Evaluation

Problem:
Optimize click rate of delivering ads
We want to estimate the probability the ads will be clicked
● request configuration
● proposed creative
● user history
● third-party information
depending on:

Algorithm:
Random Forest
Averaging the decisions
from all the trees
os
Categorie City
Oui Non OuiNon
adType
adSize weekDay
Oui Non OuiNon

Raw data
{
"time":"2016-06-09T0:25:28Z",
"bidfloor":2.88,
"appOrSite":"app",
"adType":"banner",
"categories":"games,news,football",
"carrier":"208-10",
"os":"iOS",
"connectionType":3,
"coords":[48.929256439208984, 2.4255824089050293],
"adSize":[320, 50],
"exchange":"xxxxx",
[...],
"clicked":true
}
Sampling of 13 Gb

Os MaxPrice Time
Android 7.3 2016-06-09T0:25:28Z
iOS 4.55 2016-05-09T14:23:12Z
WindowsPhone 2.89 2016-06-09T11:35:11Z

Os MaxPrice Time
Android 7.3 2016-06-09T0:25:28Z
iOS 4.55 2016-05-09T14:23:12Z
WindowsPhone 2.89 2016-06-09T11:35:11Z
Click
False
True
False

Os MaxPrice Time
Android 7.3 2016-06-09T0:25:28Z
iOS 4.55 2016-05-09T14:23:12Z
WindowsPhone 2.89 2016-06-09T11:35:11Z
Click
False
True
False
Os MaxPrice Time
3.0 6.0 1.0
5.0 3.0 5.0
1.0 2.0 3.0

Preprocessing: Spark ml
Extraction: Extracting features from “raw” data
Transformation: Scaling, converting, or modifying features
Selection: Selecting a subset from a larger set of features

Preprocessing: Spark ml
Extraction: Extracting features from “raw” data
TF-IDF, SparkSQL
Transformation: Scaling, converting, or modifying features
Bucketizer, String Indexer, Index to String, Vector Assembler
Selection: Selecting a subset from a larger set of features
ChiSqSelector

Preprocessing: Saddle
array-backed, specialized data structures:
Pandas-like operations:
dealing with missing values
index transformation tools
extracting,slicing,mapping
row/column wise
groupBy/join/concat
sorting/pivoting

Learning: Spark ml
Dataframe-based API
Classification
Regression
Linear Methods
Decision Trees
Tree ensembles

Learning: Spark ml
Dataframe-based API
Pipeline interface
Classification
Regression
Linear Methods
Decision Trees
Tree ensembles
TF-IDF String Indexer Assembler Random Forest Evaluation

Learning: Smile
Classification
Regression
Linear Methods
Decision Trees
Tree ensembles
Array-backed API

Learning: Smile
Classification
Regression
Linear Methods
Decision Trees
Tree ensembles
★ Visualisation
★ Missing Values Imputation
★ Association Rule Mining
★ Manifold learning
★ Multi-dimensional scaling
★ Feature selection and dimensionality reduction

Preprocessing: Saddle
Create dataframe and balance the data

Preprocessing: Spark ml
Create dataframe and balance the data

Preprocessing: Spark ml
Index categorical data
timestamp os osIdx
1465037789 iOS 1
1464983457 Windows Phone 2
1465019529 Android 0
1464974567 iOS 1
1465018552 Android 0

Preprocessing: Saddle
Index categorical data

Preprocessing: Saddle
Split randomly to test and train sets
and convert to input type needed in Smile RF implementation

Preprocessing: Spark ml
Conversion and sampling

Learning:
Smile
Construct Classifier and set
hyperparameters
Spark ml

Learning: Train model
and predict on test dataframe
Spark ml
Smile

Compare Spark and Smile Random Forest
The higher the better The lower the better
Classification metrics

Compare Spark and Smile Random Forest
Running time on 13 GB
minutes

My List[tools] for THIS project:
Preprocessing
Spark
Machine Learning
(Random Forest)
Smile

Your Option[tools] for YOUR project:
Spark
SMILE
Saddle

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Il y a 20 ans, l’Extreme Programming était un framework novateur avec des pratiques de génie logiciel sans lesquelles nous n’imaginerions plus travailler aujourd’hui pour produire des logiciels de qualité. Dans cette présentation, nous découvrirons comment les pratiques de l’Extrême Data-Science, qui se reposent sur les épaules du géant Extrême Programming, nous permettent d’intégrer avec succès les data-scientists et leurs projets dans les équipes, et aident à assurer la qualité des livrables data-science qui offrent des fonctionnalités optimales pour l’utilisateur.

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Il n'est pas évident d'intégrer de la Data Science dans les sociétés qui développent un business qui de base ne prévoyait pas de l’intelligence artificielle (IA), et pour lequel l’IA n'est pas au cœur du métier. Malgré la motivation d'utiliser l’IA, de nombreux projets Data Science dans ces sociétés échouent. C'est autant frustrant pour les responsables d'entreprises que démotivant pour les data-scientists, dont les projets finissent au placard. On va analyser ensemble cette situation, pour déterminer les raisons de ces échecs. On va également étudier comment éviter les erreurs les plus courantes, et comment mener ce changement sans encombre afin d'enrichir vos produits avec l’IA. L’objectif du talk est que peu importe le profil que vous avez - dev front, dev back, data-scientist, CTO, CEO, Product Manager - vous retournerez lundi dans votre société en sachant à la fois identifier et mener à bien les opportunités de Data Science.

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«Нужно бежать со всех ног, чтобы только оставаться на месте, а чтобы куда-то попасть, надо бежать как минимум вдвое быстрее!» — data scientist в Стране Чудес. Наука о данных вынуждена, как минимум, идти в ногу с постоянно увеличивающимися объемами и сложностью данных, а в идеальном случае стараться опережать и предупреждать потенциальные проблемы, возникающие при их обработке. В этом докладе вы увидите, как Scala-библиотеки Saddle, Smile и Spark помогают науке о данных отвечать постоянно эволюционирующим требованиям инфраструктуры, облегчая анализ и расширяя возможности описательной статистики, обработки данных и машинного обучения. В этом им помогают функциональные аспекты языка Scala, его благоприятная экосистема больших данных и гибридность с объектно-ориентированным программированием. На примере предсказания кликов на рекламных пространствах интернета мы исследуем с вами возможности, преимущества и пути развития Scala для науки о данных.

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This project aims to predict whether a loan application will be approved or denied based on various factors such as applicant's income, credit score, loan amount, etc. Using a dataset containing historical loan application data, we employed machine learning algorithms to build a predictive model. The model was trained on features such as applicant's income, credit history, loan amount, loan term, and others. After training the model, we evaluated its performance using metrics like accuracy, precision, recall, and F1 score. The insights from this project can help financial institutions streamline their loan approval process and make informed decisions. Visit for more information: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

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DATA SUMMIT 24 Building Real-Time Pipelines With FLaNK

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This project demonstrates a machine learning approach to detecting credit card fraud using advanced algorithms and techniques. The project utilizes a dataset containing various features such as transaction amount, merchant location, time of transaction, and others to build a predictive model. The presentation covers data preprocessing steps, feature engineering techniques, and the selection of machine learning algorithms such as logistic regression or random forest. It also discusses model evaluation metrics and the importance of fraud detection in financial institutions for safeguarding against fraudulent activities. Visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

Detecting Credit Card Fraud: A Machine Learning Approach

Boston Institute of Analytics

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Big Data Science in Scala

1. Big Data Science in Scala Anastasia Lieva Data Scientist @lievAnastazia

2. 1. R 2. Python 3. SQL 2014 KDnuggets Polls: most popular tools in data-science 2015 2016

3. Context: Real Time Bidding Raw requests: 100 000 requests per second 4 terabytes per day

4. R Python SQL Scala

5. R Python SQL Scala Spark ML/DATAFRAME/SQL SMILE Saddle

6. Spark Saddle Smile Preprocessing Machine Learning Evaluation Preprocessing Machine Learning Evaluation

7. Problem: Optimize click rate of delivering ads We want to estimate the probability the ads will be clicked ● request configuration ● proposed creative ● user history ● third-party information depending on:

8. Algorithm: Random Forest Averaging the decisions from all the trees os Categorie City Oui Non OuiNon adType adSize weekDay Oui Non OuiNon

9. Raw data { "time":"2016-06-09T0:25:28Z", "bidfloor":2.88, "appOrSite":"app", "adType":"banner", "categories":"games,news,football", "carrier":"208-10", "os":"iOS", "connectionType":3, "coords":[48.929256439208984, 2.4255824089050293], "adSize":[320, 50], "exchange":"xxxxx", [...], "clicked":true } Sampling of 13 Gb

10. Os MaxPrice Time Android 7.3 2016-06-09T0:25:28Z iOS 4.55 2016-05-09T14:23:12Z WindowsPhone 2.89 2016-06-09T11:35:11Z

11. Os MaxPrice Time Android 7.3 2016-06-09T0:25:28Z iOS 4.55 2016-05-09T14:23:12Z WindowsPhone 2.89 2016-06-09T11:35:11Z

12. Os MaxPrice Time Android 7.3 2016-06-09T0:25:28Z iOS 4.55 2016-05-09T14:23:12Z WindowsPhone 2.89 2016-06-09T11:35:11Z

13. Os MaxPrice Time Android 7.3 2016-06-09T0:25:28Z iOS 4.55 2016-05-09T14:23:12Z WindowsPhone 2.89 2016-06-09T11:35:11Z

14. Os MaxPrice Time Android 7.3 2016-06-09T0:25:28Z iOS 4.55 2016-05-09T14:23:12Z WindowsPhone 2.89 2016-06-09T11:35:11Z Click False True False

15. Os MaxPrice Time Android 7.3 2016-06-09T0:25:28Z iOS 4.55 2016-05-09T14:23:12Z WindowsPhone 2.89 2016-06-09T11:35:11Z Click False True False Os MaxPrice Time 3.0 6.0 1.0 5.0 3.0 5.0 1.0 2.0 3.0

16. Preprocessing: Spark ml Extraction: Extracting features from “raw” data Transformation: Scaling, converting, or modifying features Selection: Selecting a subset from a larger set of features

17. Preprocessing: Spark ml Extraction: Extracting features from “raw” data TF-IDF, SparkSQL Transformation: Scaling, converting, or modifying features Bucketizer, String Indexer, Index to String, Vector Assembler Selection: Selecting a subset from a larger set of features ChiSqSelector

18. Preprocessing: Saddle array-backed, specialized data structures: Pandas-like operations: dealing with missing values index transformation tools extracting,slicing,mapping row/column wise groupBy/join/concat sorting/pivoting

19. Learning: Spark ml Dataframe-based API Classification Regression Linear Methods Decision Trees Tree ensembles

20. Learning: Spark ml Dataframe-based API Pipeline interface Classification Regression Linear Methods Decision Trees Tree ensembles TF-IDF String Indexer Assembler Random Forest Evaluation

21. Compare performance : Spark

22. Learning: Smile Classification Regression Linear Methods Decision Trees Tree ensembles Array-backed API

23. Learning: Smile Classification Regression Linear Methods Decision Trees Tree ensembles ★ Visualisation ★ Missing Values Imputation ★ Association Rule Mining ★ Manifold learning ★ Multi-dimensional scaling ★ Feature selection and dimensionality reduction

24. Preprocessing: Saddle Create dataframe and balance the data

25. Preprocessing: Spark ml Create dataframe and balance the data

26. Preprocessing: Spark ml Index categorical data timestamp os osIdx 1465037789 iOS 1 1464983457 Windows Phone 2 1465019529 Android 0 1464974567 iOS 1 1465018552 Android 0

27. Preprocessing: Saddle Index categorical data

28. Preprocessing: Saddle Split randomly to test and train sets and convert to input type needed in Smile RF implementation

29. Preprocessing: Spark ml Conversion and sampling

30. Learning: Smile Construct Classifier and set hyperparameters Spark ml

31. Learning: Train model and predict on test dataframe Spark ml Smile

32. Learning: Evaluate model Spark ml Smile

33. Compare Spark and Smile Random Forest The higher the better The lower the better Classification metrics

34. Compare Spark and Smile Random Forest Running time on 13 GB minutes

35. Compare preprocessing: Spark vs Saddle

36. My List[tools] for THIS project: Preprocessing Spark Machine Learning (Random Forest) Smile

37. Your Option[tools] for YOUR project: Spark SMILE Saddle