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Solving ILP Problems in the EELA infrastructure

Inês DutraDepartamento de Ciência de

ComputadoresUniversidade do Porto, Portugal

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Outline

• Introduction– ILP– Examples– Motivation

• Experiments• Conclusions• Future Work

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Introduction

• EELA selected application• Task 3.3: additional applications

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Introduction

• What is ILP?– It is NOT Instruction Level Parallelism– It is NOT Integer Linear Programming

• So, what is it????• .......

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Introduction• It is Inductive Logic Programming

– data mining – machine learning– Knowledge/information extraction

• Where:– Given:

• Set of observations (positive and negative)• Background knowledge (descriptions)• Language bias

– Find:• A hypothesis (in first order language) that best explains all

positive observations and none of the negatives.

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Introduction

• Advantages:– Use of an understandable description

language– Relational knowledge

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Introduction: example

TRAINS GOING EAST TRAINS GOING WEST

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Introduction: example

short(car_12).closed(car_12).long(car_11).long(car_13).short(car_14).open_car(car_11).open_car(car_13).open_car(car_14).shape(car_11,rectangle). shape(car_12,rectangle).shape(car_13,rectangle).shape(car_14,rectangle).

load(car_11,rectangle,3). load(car_12,triangle,1).load(car_13,hexagon,1).load(car_14,circle,1).wheels(car_11,2).wheels(car_12,2).wheels(car_13,3).wheels(car_14,2).has_car(east1,car_11).has_car(east1,car_12).has_car(east1,car_13).has_car(east1,car_14).

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Introduction: example

TRAINS GOING EAST TRAINS GOING WEST

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Introduction: example

eastbound(T) IF has_car(T,C) AND short(C) AND closed(C)

TRAINS GOING EAST TRAINS GOING WEST

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Another less “toyish” example: extracting knowledge from mammograms

is_malignant(A) if 'BIRADS_category'(A,b5), 'MassPAO'(A,present), 'Age'(A,age6570), previous_finding(A,B,C), 'MassesShape'(B,none), 'Calc_Punctate'(B,notPresent), previous_finding(A,C), 'BIRADS_category'(C,b3).

This rule states that finding (A) IS malignant IF it is:

classified as BI-RADS 5 ANDhad a mass presentin a patient who: was between the ages of 65 and 70 had two prior mammograms (B, C)and prior mammogram (B): had no mass shape described had no punctate calcificationsand prior mammogram (C) was classified as BI-RADS 3

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Introduction: Motivation

• Applications:– Link discovery– Social Network Analysis– Equivalent identities– Drug design– Protein unfolding– Protein metabolism– Why not? Classifying grid failures ()– And...many others!

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Introduction: Motivation

• Why does ILP need a grid?– Search space can become large very

quickly– Need many experiments to have statistical

significant results• Cross-validation• Training, tuning, testing

– Can combine classifiers: ensembles

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Introduction: Motivation

• Assume we want to run a task for one domain: find a “good” hypothesis that describes pos examples

• Assume we run 5x4-fold cross-validation• Assume we have 100 classifiers per fold• # of experiments: 2,000

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Introduction: Motivation

• Now assume each experiment takes 1 hour to run

• How long would it take to generate the 2,000 classifiers to be combined?

~ 83 days!!!• If we consider varying learning parameters

and learning algorithms, this number can be really big!!

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Experiment

• Predict carcinogenecity in rodents– Difficult task– large search space!– Important problem

• Phase 1:– Tuning using 5x4-fold cross-validaton– Generating ensembles up to 100

• Aleph: well-known ILP system• Yap: Yet another prolog

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Experiment: one of the classifiers

active(A) if atom(A,_,n,32,B), B ≤ -0.401, has_property(A,cytogen_sce,n), methyl(A,_).

Sister Chromatid Exchange (SCE)SCE is used for the determination of mutagenity

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Experiment

• 2 submissions:– From LA– From EU

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Submitting jobs from LA....

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Experiment

EELA resources

utilised

Resource # of jobsCERN 1,160

CIEMAT 279CETA-CIEMAT 173

UniCan 98LIP 10

INFN 38UNAM 16

BIOF.UFRJ 159IF.UFRJ 8UFCG 28Total 1,969

~ 300 resources in LA

211 jobs in LA

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Experiments

• Why 1,969 out of 2,000???• 2 reasons:

– Proxy expiration:• On submission (takes loooooong!!!)• On execution

– Use of dynamic libraries

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• Submitting jobs from EU...• from a non-EELA site, BUT• Using the EELA VO:

– Jobs run only on EU resources...• Reasons:

– Misconfiguration?– Closer brokers with more machines?

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Conclusions• Happiness: EELA is working!!!• We can run thousands of experiments!• Frida is happy!!! (see Condor introductory

tutorials, if you feel curious about Frida )• Experiment showed good utilization of EELA

resources in LA and EU• Low failure rate (1%)• Failures motivated by:

– Dynamic libs not available in the remote machine– Proxy expiration

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Future work

• More detailed analysis of jobs and logs• Full ILP experiment• More domains• Other kinds of experiments based on

Statistical Relational Learning

• And, do not forget: ILP can help to model and diagnose errors in the grid environment!

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Collaborators

• Fernando Silva (DCC-UPorto)• Vítor Santos Costa (DCC-UPorto)• Rui Camacho (FE-UPorto)• Nuno Fonseca (IBMC/IBMEC, Porto)• Beth Burnside (UW-Madison hospital)• David Page (UW-Madison)• Jesse Davis (UWashington)

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Thanks!!!

Questions??