Moving Forward ThroughThe Darkness

the blindness of modeling and how to break through

Chia-Chi@PyData SF 2016

About Chia-Chi (George) ● Organizer of Taiwan R User Group and MLDM Monday● 7 years experience in quantitative trading in future & option market● 5 years consultant experience in machine learning & data mining● 4 years experience in e-commerce (consultant & join SaaS teams)● 4 years experience in building of recommendation and search engine ● Volenteer in PyCon APAC 2014 (program officer)● Volenteer in PyCon APAC 2015 (program officer)● I love python and hope I can write python everyday !

Training models from data

is just like scketching pictures from the world

Jackson Pollock：The painting has a life of its own.

I try to let it come through.

As a data scientist … The data has a life of its own. I just try to let it come through.

The first step … is not picking up your pen !

is choosing an angle !and do some observation !

Now, the object you want to sckeching ...is your data!

Try to scketch it : y = a_0 + a_1 x

Which line is the most similar one ?

It depends on you observation angle!

What the angle means ...in a machine learning problem ?

Angle of Linear Regression

After Chose an angle ... You chose the question & the evaluator ...

(as a data scientist)

How to change the angle?

In the Linear Regression Problem

The MetaphorData (the object) +

Evaluator (view of point | angle)=> Model (picture)

Different Angles ... Different Pictures …

(in scketching)

Different questions ... Different models …

(in data science)

Whatever you observe … Whatever you draw !

(both in scketching and data science)

The two keysHelp you apply machine learning

in the real world

Can Learn ONLYThrough Real

Practice

Can Learn fromSchool or Practice

Modeling Procedures:● Choose a Real Problem● Collecting Related Data● Choose a method convert Data to Vectors (or Tensors)● Decompose Real Problem into several ML or Math Problems● Solve each ML or Math Problem individually ● Combine the Solutions of all ML or Math Problems● Check is that truly solve the Real Problem ?

Case Study:How to build

a Recommendation System in News Platform

User-Centered Recommendation

News you probabily also want to read

Platform

Tracking

Users Behavior

Data Feed Response

News Data Results

Machine Learning

Server Group

Server Group

Processing Data Prediction Data

Modeling Procedures -- Part 1:● Problem: how to make users reach more news they want to read ?● Data:

○ News Data (Article)■ Title■ Text■ Time■ Category

○ User Behavior Data ■ User View Post■ User Click Links■ User not Click Links

Modeling Procedures -- Part 2:● Data to Vector (or Tensors)

○ News Data■ TermDocumentMatrix (scikit-learn)■ Word2Vec (gensim word2vec)

○ User Behavior Data ■ Event Sampling (Spark streaming, Kafka, or Traildb)

● construct user-item matrix (user view|click|not-click events)● construct item-item matrix (view-after-view click-after-view ... )● construct user-item-time tensor cube ● construct user-item-item-time tensor cube

Modeling Procedures -- Part 3:● Real to ML (or Math) and Solve ML (or Math) Problems

○ Real Problem: how to make users reach more news they want to read ?○ ML (or Math) Problems:

■ Hottest & Newest ■ Content-Based Relations■ Collaborative Filtering

Newest & Hottest : Sorting

Content-Based Relations: ClusteringWith

TermDocumentMatrix

Or

The results coming from word2vec

Collaborative Filtering: MF & Matrix Completion

Use only 20% data to re-generate full image !

Ref: ipynb@github

Modeling Procedures -- Part 4:● Combine Solutions together and Check it with Real Problem

○ Ensemble Learning (for static combination)○ Reinforcement Learning (for dynamic improvement)

Recap: Modeling Procedures:● Choose a Real Problem● Collecting Related Data● Choose a method convert Data to Vectors (or Tensors)● Decompose Real Problem into several ML or Math Problems● Solve each ML or Math Problem individually ● Combine the Solutions of all ML or Math Problems● Check is that truly solve the Real Problem ?

The Blindnessin the Modeling Procedures

Blindness of Modeling Procedures:

● Choose a Real Problem● Collecting Related Data● Choose a method convert Data to Vectors (or Tensors)● Decompose Real Problem into several ML or Math Problems● Solve each ML or Math Problem individually ● Combine the Solutions of all ML or Math Problems● Check is that truly solve the Real Problem ?

Problem Data

Probelm-Driven:

Thinking Data

Through Problem

Data-Driven:Thinking Problem

Through Data

Problem behind

Problem

Information behind Data

BusinessInsights

The Blindness betweenData and Problem

Is there any related information in that data ?Could the problem answer by this data ?

Case Study: Bookstore Could you use our POS data

to find some methods to convertthose users who originally dislike us?

Case Study: Bookstore Could you use our POS data to find the potential users ?

"potential" means users want to buy itbut they haven't

Data from POSONLY has the information

about converted users

There is no information about disliked and unconverted users

Thinking in Two WaysData-Driven

Problem-Driven

Problem???

POSData

Probelm-Driven:

Thinking Data

Through Problem

Data-Driven:Thinking Problem

Through Data

Problem behind

Problem

Information behind Data

BusinessInsights

Gain Bookstore's Revenue ?

Data???

Probelm-Driven:

Thinking Data

Through Problem

Data-Driven:Thinking Problem

Through Data

Problem behind

Problem

Information behind Data

BusinessInsights

Case Study: LBS Food Search

a story about "delicous" is not delicous !

(this is also the blindness of NLP)

Data ThinkingFirst-Hand Versus Second-Hand

for example, delicous versus "delicous"

Machine could NOT Learn by itself.

It just like a child.It learn by training data !

sometimes would learn badly!

Blindness of Modeling Procedures:● Choose a Real Problem● Collecting Related Data

● Choose a method convert Data to Vectors (or Tensors)

● Decompose Real Problem into several ML or Math Problems● Solve each ML or Math Problem individually ● Combine the Solutions of all ML or Math Problems● Check is that truly solve the Real Problem ?

The Blindness FromData to Vector

Is there any information losing when you are converting your data?

Blindness of unigramI love it (我愛它) = it love me (它愛我)I hate it (我恨它) = it hate me (它恨我)

The Blindness FromMathematical Concept

The gap between math and real world:

When putting the units back to the formula …

Math in Elementary School … The secret behind the minus operator● 103 - 100 = 6 - 3 ?● 103 dollars - 100 dollars = 6 dollars - 3 dollars ?● 103 dollar stock - 100 dollars stock = 6 dollars stock - 3 dollars stock ?

(formula)(units)

● (103 - 100 = 6 - 3) (dollars)● (103 - 100 = 6 - 3) (dollars stock)

How to choose the right coordinate for stock price ?

Blindness of Modeling Procedures:● Choose a Real Problem● Collecting Related Data● Choose a method convert Data to Vectors (or Tensors)

● Decompose Real Problem into several ML or Math Problems

● Solve each ML or Math Problem individually ● Combine the Solutions of all ML or Math Problems● Check is that truly solve the Real Problem ?

The Blindness FromML Frameworks

Classification & Clustering

When orange-apple classifier meet an banana?

When a digital classifier meet an alphabet ?

A -> 9

The blindness of clustering methods

Cannot force two points in the same cluster

The fact is … We always get some data with labels

But some without

(1) How to propograte labels ?(2) How to detect new labels with labelers?

New Data & New Labelsare coming all the way

In e-commerce retailers &In news platforms

What we need … (1) Classifier just like a clustering method

(one-versus-all incremental classifier)

(2) Clustering Method just like a Classifier(Metric Learning)

one-versus-all incremental classifier

NotClass 1

NotClass 2

Metric Learning (shugon)

Actually … You can also use deep neural network

to construct the metric learning staff

Metric LearningAlways give me a whole new angle

to observe the world

Remember that ... !Whatever you observe …

Whatever you draw !

Thanks for your attention!