Brazilian Summer School in Machine Learning 2016 Day 2 - Lecture 1: Basic Data Transformations Lecturer: Poul Petersen (BigML)

1. D E C E M B E R 8 - 9 , 2 0 1 6

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Poul Petersen
CIO, BigML, Inc.
Basic TransformationsCreating Machine Learning Ready Data

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In a Perfect World…
Q: How does a physicist milk a cow?
A: Well, first let us consider a spherical cow...
Q: How does a data scientist build a model?
A: Well, first let us consider perfectly formatted data…

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The Dream
CSV Dataset Model Profit!

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The Reality
CRM
Web Accounts
Transactions
ML Ready?

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Obstacles
• Data Structure
• Scattered across systems
• Wrong "shape"
• Unlabelled data
• Data Value
• Format: spelling, units
• Missing values
• Non-optimal correlation
• Non-existant correlation
• Data Significance
• Unwanted: PII, Non-Preferred
• Expensive to collect
• Insidious: Leakage, obviously correlated
Data Transformation
Feature Engineering
Feature Selection

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The Process
• Define a clear idea of the goal.
• sometimes this comes later…
• Understand what ML tasks will achieve the goal.
• Transform the data
• where is it, how is it stored?
• what are the features?
• can you access it programmatically?
• Feature Engineering: transform the data you have into the data
you actually need.
• Evaluate: Try it on a small scale
• Accept that you might have to start over….
• But when it works, automate it!!!!

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Data Transformations

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BigML Tasks
Goal
• Will this customer default on a loan?
• How many customers will apply for a
loan next month?
• Is the consumption of this product
unusual?
• Is the behavior of the customers
similar?
• Are these products purchased
together?
ML Task
Classification
Regression
Anomaly Detection
Cluster Analysis
Association Discovery

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Classification
CategoricalTrainingTesting
Predicting

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Regression
NumericTrainingTesting
Predicting

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Anomaly Detection

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Cluster Analysis

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Association Discovery

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Data Labelling
Data may not have labels needed for doing classification
Create specific metrics for adding labels
Name Month - 3 Month - 2 Month - 1
Joe Schmo 123,23 0 0
Jane Plain 0 0 0
Mary Happy 0 55,22 243,33
Tom Thumb 12,34 8,34 14,56
Un-Labelled Data
Labelled data
Name Month - 3 Month - 2 Month - 1 Default
Joe Schmo 123,23 0 0 FALSE
Jane Plain 0 0 0 TRUE
Mary Happy 0 55,22 243,33 FALSE
Tom Thumb 12,34 8,34 14,56 FALSE
Can be done at Feature
Engineering step as well

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ML Ready DataInstances
Fields (Features)
Tabular Data (rows and columns):
• Each row
• is one instance.
• contains all the information about that one instance.
• Each column
• is a field that describes a property of the instance.

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SF Restaurants Example
https://data.sfgov.org/Health-and-Social-Services/Restaurant-Scores/stya-26eb
https://blog.bigml.com/2013/10/30/data-preparation-for-machine-learning-using-mysql/
create database sf_restaurants;
use sf_restaurants;
create table businesses (business_id int, name varchar(1000), address varchar(1000), city varchar(1000), state varchar(100),
postal_code varchar(100), latitude varchar(100), longitude varchar(100), phone_number varchar(100));
load data local infile './businesses.csv' into table businesses fields terminated by ',' enclosed by '"' lines terminated by
'rn' ignore 1 lines;
create table inspections (business_id int, score varchar(10), idate varchar(8), itype varchar(100));
load data local infile './inspections.csv' into table inspections fields terminated by ',' enclosed by '"' lines terminated
by 'rn' ignore 1 lines;
create table violations (business_id int, vdate varchar(8), description varchar(1000));
load data local infile './violations.csv' into table violations fields terminated by ',' enclosed by '"' lines terminated by
'rn' ignore 1 lines;
create table legend (Minimum_Score int, Maximum_Score int, Description varchar(100));
load data local infile './legend.csv' into table legend fields terminated by ',' enclosed by '"' lines terminated by 'rn'
ignore 1 lines;

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Data Cleaning
Homogenize missing values and different types in the same
feature, fix input errors, correct semantic issues, types, etc.
Name Date Duration (s) Genre Plays
Highway star 1984-05-24 - Rock 139
Blues alive 1990/03/01 281 Blues 239
Lonely planet 2002-11-19 5:32s Techno 42
Dance, dance 02/23/1983 312 Disco N/A
The wall 1943-01-20 218 Reagge 83
Offside down 1965-02-19 4 minutes Techno 895
The alchemist 2001-11-21 418 Bluesss 178
Bring me down 18-10-98 328 Classic 21
The scarecrow 1994-10-12 269 Rock 734
Original data
Name Date Duration (s) Genre Plays
Highway star 1984-05-24 Rock 139
Blues alive 1990-03-01 281 Blues 239
Lonely planet 2002-11-19 332 Techno 42
Dance, dance 1983-02-23 312 Disco
The wall 1943-01-20 218 Reagge 83
Offside down 1965-02-19 240 Techno 895
The alchemist 2001-11-21 418 Blues 178
Bring me down 1998-10-18 328 Classic 21
The scarecrow 1994-10-12 269 Rock 734
Cleaned data
update violations set description = substr(description,1,instr(description,' [ date violation corrected:')-1) where instr(description,'
[ date violation corrected:') > 0;

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Denormalizing
business
inspections
violations
scores
Instances
Features
(millions)
join
Data is usually normalized in relational databases, ML-Ready datasets
need the information de-normalized in a single file/dataset.
create table scores select * from inspections left join businesses using (business_id);

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Aggregating
User Num.Playbacks Total Time Pref.Device
User001 3 830 Tablet
User002 1 218 Smartphone
User003 3 1019 TV
User005 2 521 Tablet
Aggregated data (list of users)
When the entity to model is different from the provided data, an
aggregation to get the entity might be needed.
Content Genr
e
Duration Play Time User Device
Highway
star
Rock 190 2015-05-12
16:29:33
User001 TV
Blues alive Blues 281 2015-05-13
12:31:21
User005 Tablet
Lonely
planet
Tech
no
332 2015-05-13
14:26:04
User003 TV
Dance,
dance
Disco 312 2015-05-13
18:12:45
User001 Tablet
The wall Reag
ge
218 2015-05-14
09:02:55
User002 Smartphone
Offside
down
Tech
no
240 2015-05-14
11:26:32
User005 Tablet
The
alchemist
Blues 418 2015-05-14
21:44:15
User003 TV
Bring me
down
Class
ic
328 2015-05-15
06:59:56
User001 Tablet
The
scarecrow
Rock 269 2015-05-15
12:37:05
User003 Smartphone
Original data (list of playbacks)
select business_id,vdate,count(*) as violation_num,group_concat(description) as violation_txt from violations group by business_id, vdate;
select * from scores left join violations_aggregated ON (scores.business_id=violations_aggregated.business_id) AND ( vdate=idate);
tail -n+2 playlists.csv | cut -d',' -f5 | sort | uniq -c
tail -n+2 playlist.csv | awk -F',' '{arr[$5]+=$3} END {for (i in arr) {print arr[i],i}}'

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Pivoting
Different values of a feature are pivoted to new columns in the
result dataset.
Content Genre Duration Play Time User Device
Highway star Rock 190 2015-05-12 16:29:33 User001 TV
Blues alive Blues 281 2015-05-13 12:31:21 User005 Tablet
Lonely planet Techno 332 2015-05-13 14:26:04 User003 TV
Dance, dance Disco 312 2015-05-13 18:12:45 User001 Tablet
The wall Reagge 218 2015-05-14 09:02:55 User002 Smartphone
Offside down Techno 240 2015-05-14 11:26:32 User005 Tablet
The alchemist Blues 418 2015-05-14 21:44:15 User003 TV
Bring me down Classic 328 2015-05-15 06:59:56 User001 Tablet
The scarecrow Rock 269 2015-05-15 12:37:05 User003 Smartphone
Original data
User Num.Playback
s
Total Time Pref.Device NP_TV NP_Tablet NP_Smartphone TT_TV TT_Tablet TT_Smartphone
User001 3 830 Tablet 1 2 0 190 640 0
User002 1 218 Smartphone 0 0 1 0 0 218
User003 3 1019 TV 2 0 1 750 0 269
User005 2 521 Tablet 0 2 0 0 521 0
Aggregated data with pivoted columns

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Time Windows
Create new features using values over different periods of time
Instances
Features
Time
Instances
Features
(millions)
(thousands)
t=1 t=2 t=3
select business_id, score as score_2013, idate as idate_2013 from inspections where substr(idate,1,4) = "2013";
create table scores_over_time select * from businesses left join scores_2013 USING (business_id) left join scores_2014 USING (business_id);

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Updates
Need a current view of the data, but new data only comes in
batches of changes
day 1day 2day 3
Instances
Features

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Streaming
Data only comes in single changes
data stream
Instances
Features
Stream
Batch
(kafka, etc)

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Structuring Output
• A CSV file uses plain text to store tabular data.
• In a CSV file, each row of the file is an instance.
• Each column in a row is usually separated by a comma (,) but other
"separators" like semi-colon (;), colon (:), pipe (|), can also be used. Each
row must contain the same number of fields
• but they can be null
• Fields can be quoted using double quotes (").
• Fields that contain commas or line separators must be quoted.
• Quotes (") in fields must be doubled ("").
• The character encoding must be UTF-8
• Optionally, a CSV file can use the first line as a header to provide the
names of each field.
After all the data transformations, a CSV (“Comma-Separated
Values) file has to be generated, following the rules below:

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Prosper Loan Life Cycle
Submit Bids
Cancelled Withdraw
Funded
Expired
Defaulted
Paid
Current
Late
Q: Which new loans make it to funded?
Q: Which funded loans make it to paid?
Q: If funded, what will be the rate?
Classification
Regression
Classification
Goal ML Task

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Prosper Example
Data Provided in XML updates!!
fetch.sh
“curl”
daily
export.sh
import.py
XML
bigml.sh
Model
Predict
Share in gallery
Status
LoanStatus
BorrowerRate

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Prosper Example
• XML… yuck!
• MongoDB has CSV export and is record based so it is easy to
handle changing data structure.
• Feature Engineering
• There are 5 different classes of “bad” loans
• Date cleanup
• Type casting: floats and ints
• Would be better to track over time
• number of late payments
• compare predictions and actuals
• XML… yuck!
Tidbits and Lessons Learned….

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Tools
• Command Line?
• join, cut, awk, sed, sort, uniq
• Automation
• Shell, Python, etc
• Talend
• BigML: bindings, bigmler, API, whizzml
• Relational DB
• MySQL
• Non-Relational DB
• MongoDB

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Talend
https://blog.bigml.com/2013/10/30/data-preparation-for-machine-learning-using-mysql/
Denormalization Example