BigML Release: Data Transformations

Data Transformations Release
Introducing
Data Transformations

BigML, Inc BigML Data Transformations Release Webinar
Data Transformations
POUL PETERSEN M.SC.- Chief Infrastructure Ofﬁcer
Please enter questions into chat box – We will answer
some via chat and others at the end of the session
https://bigml.com/releases/summer-2018
ATAKAN CETINSOY - VP of Predictive Applications
Resources
Moderator
Speaker
Contact support@bigml.com
Twitter @bigmlcom
Questions
!2

Reality of a ML Application
Data

Transformations
Feature

Engineering
Data

Collection
Evaluation

& Retraining
Seen
Unseen
Self-Driving Cars?
!3

Effort of a ML Application
State the problem as an ML task
Data wrangling
Feature engineering
Modeling and Evaluations
Predictions
Measure Results
Data transformations ~80% effort
~5% effort
~5% effort
This is only such low
effort because of
platforms like
Today’s release is the
first step towards
making this
easy as well!
Task
~10% effort
Effort
!4

Problem Statement
• BigML’s SaaS https://bigml.com builds, on average, >40,000 trees/day
• That’s trees only! Not counting LR, deepnets, clusters, etc.
• And bigml.com only - not bigml.com.au as well
• We need to ensure that all models are started and ﬁnished ASAP
• Started is “easy”: queue monitoring + auto-scaling + heuristics
• Finished is harder: How do we know if a model is taking too long?
• What if we could predict how long a model should take to build?
• Generate alarm if it takes longer than, e.g. 120% of the predicted time
This sounds like a Machine Learning problem!!!
!5

The Data…
• Metadata from dataset :
• Size in bytes, number of rows, number of columns, etc.
• Number of numeric, categorical, datetime, text, and items ﬁelds
• Metadata from model:
• Objective type: classiﬁcation or regression
• Tree options: node_depth, missing_splits, randomization, sample
• Subcluster: relates to server size
• Objective:
• Time elapsed to build the tree
!6

The Data
!7

Problem #1
There may be identical feature rows
• A user testing a script, re-building with the same parameters
• A Machine Learning class building the same model for an assignment
• Users following an online tutorial
• A BigML employee demoing the same dataset / model process
#numeric #text #datetime size rows … time_ms
34 0 0 46354 1001 … 300
0 1 0 1515654 373 … 1431
4 0 1 56789 445423 … 8891
… … … … … … …
0 1 0 1515654 373 … 1673
Same Different
However it happens, this is not properly formatted for ML
!8
How?

34 0 0 46354 1001 … 300
0 1 0 1515654 373 … 1431
4 0 1 56789 445423 … 8891
… … … … … … …
0 1 0 1515656 373 … 1673
feature_key time_ms
c3f6c8be4f 300
a243ca3c38 1431
14f9d917bc 8891
… …
a243ca3c38 1673
Collapse
Transform & Aggregate
All feature rows unique
(fear not SQL experts)
feature_key avg_ms
c3f6c8be4f 300
a243ca3c38 1552
14f9d917bc 8891
… …
Aggregate
How to get there…
!9

Flatline
(sha1 (str (all-but ‘status.elapsed’)))
!10

Aggregation: Count
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
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
User Count
User001 3
User005 2
User003 2
User002 1
Count
on User
Number of playbacks per user
!11

Aggregation: Count Distinct
Bring me
down
User
Distinct
Genre
User001 3
User005 2
User003 2
User002 1
Count
distinct
Genre
on User
Number of distinct Genre played per user
!12

Aggregation: Count Missing
Bring me
down
User
Missing
Device
User001 0
User005 0
User003 0
User002 1
Count
missing
Device
on User
Number of missing Device per user
!13

Aggregation: Sum
Bring me
down
User
Sum
Duration
User001 830
User005 521
User003 750
User002 218
Sum
Duration
on User
Total Duration per User
!14

Aggregation: Average
Bring me
down
User
Average
Duration
User001 276,67
User005 260,50
User003 375,00
User002 218
Average
Duration
on User
Average Duration per User
!15

Aggregation: Maximum
Bring me
down
User
Max
Duration
User001 328
User005 281
User003 418
User002 218
Maximum
Duration
on User
Maximum Duration per User
!16

Aggregation: Minimum
Bring me
down
User
Min
Duration
User001 190
User005 240
User003 332
User002 218
Minimum
Duration
on User
!17
Minimum Duration per User
• Similar for standard deviation and variance
• Possible to combine multiple aggregations on the same ﬁeld

Aggregations
!18

Problem #2
We have this…
34 0 0 46354 1001 … 300
0 1 0 1515654 373 … 1431
4 0 1 56789 445423 … 8891
… … … … … … …
0 1 0 1515656 373 … 1673
Dataset 1
feature_key avg_ms
c3f6c8be4f 300
a243ca3c38 1552
14f9d917bc 8891
… …
Dataset 2
We want this…
#numeric #text #datetime size rows … avg_ms
34 0 0 46354 1001 … 300
0 1 0 1515654 373 … 1552
4 0 1 56789 445423 … 8891
… … … … … … …
Dataset
!19

Joins
• Datasets to join need to have a ﬁeld in common
• joining sales and demographics on customer_id
• joining employee and budget details on department_id
• Datasets to join do not need to have the same dimensions
• Joins can be performed in several ways
• Left, Right, Inner, Outer…
!20

Left Join
• In a Left join of dataset A to B:
• Returns all records from the left A,  
and the matched records from B
• The result is NULL from B, if there is no match.
_id field1
1 34
2 56
3 123
4 56
5 79
A
_id field2
1 red
2 green
4 blue
6 black
BLeft join
_id field1 field2
1 34 red
2 56 green
3 123 null
4 56 blue
5 79 null
A left join B=
A B
!21
No “3” or “5”

Right Join
!22
• In a Right join of dataset A to B:
• Returns all records from the right B,  
and the matched records from A
• The result is NULL from A, if there is no match.
_id field1
1 34
2 56
3 123
4 56
5 79
A
_id field2
1 red
2 green
4 blue
6 black
BRight join
_id field2 field1
1 red 34
2 green 56
4 blue 56
6 black null
A right join B=
BA
No “6”,
“3” unused

Inner Join
• In an Inner join of dataset A to B:
• Returns only records from the left A,  
that match records from B
• If there is no match between A and B, the record is ignored
A B
_id field1
1 34
2 56
3 123
4 56
5 79
A
_id field2
1 red
2 green
4 blue
6 black
BInner join
_id field1 field2
1 34 red
2 56 green
4 56 blue
A inner join B=
!23
“3” and “5”
unused
“6” unused

Full Outer Join
• In a Full join of dataset A to B:
• Returns all records from the left A,  
and records from B
• If there is no match in either A and B, the field is null
A B
_id field1
1 34
2 56
3 123
4 56
5 79
A
_id field2
1 red
2 green
4 blue
6 black
Bfull join
_id field1 field2
1 34 red
2 56 green
3 123 null
4 56 blue
5 79 null
6 null black
A full join B=
!24
A
No “6”
No “3” or “5”

Joins
!25

Problem #3
Left join keeps all records from the left dataset
#numeric #text #datetime size rows … avg_ms
34 0 0 46354 1001 … 300
0 1 0 1515654 373 … 1552
4 0 1 56789 445423 … 8891
… … … … … … …
0 1 0 1515656 373 … 1552
Same Rows
!26

Remove Duplicates
!27

Using the API
• The UI has a limited set of data transformations
• Aggregation (limited), Joins (limited), Remove Duplicates
• More functions will be added: concat, ordering, multiple group by
• The API supports nearly full SQL syntax for transforming datasets
• Nested queries not supported (yet) - e.g. subselects
• Better way to perform workﬂow:
• SELECT 10001a, avg(000019) AS avg_status_elapsed FROM DS GROUP BY 10001a
• Can perform entire workﬂow in one SQL using multiple “group by”
!28

API Transformations
!29

12 2 0 74001 200 … 1975
1 0 1 22673 373 … 1552
1056 0 1 9231411 4352 … 7675
Problem #4
How do we add new data and retrain?
• When adding a new batch of data
• Avoid re-uploading by using a merge
• Repeat the entire workﬂow on the merged dataset using Scriptify
34 0 0 46354 1001 … 300
0 1 0 1515654 373 … 1552
4 0 1 56789 445423 … 8891
!30

Merging & Scriptify
!31

https://bigml.com/releases/summer-2018
More Info
!32

Questions?
@bigmlcom support@bigml.com

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