MEX Interfaces: Automating Machine Learning Metadata Generation

MEX Interfaces: Automating Machine
Learning Metadata Generation
D. Esteves, Pablo N. Mendes, D. Moussallem, J.C. Duarte, Amrapali Zaveri,
Jens Lehmann, Ciro Baron Neto, Igor Costa and Maria Claudia Cavalcanti
University of Leipzig
September 13, 2016
1

Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016
What’s metadata and why is it so important?
“Metadata is data that provides
information about other data”
- Data Management
- Meta Analysis (ML)
- Social Engines
- ...
2

=== Run information ===
Scheme:weka.classifiers.trees.J48 -C 0.25 -M 2
Relation: iris
Instances: 150
Attributes: 5
sepallength
sepalwidth
petallength
petalwidth
class
Test mode:evaluate on training data
...
TP Rate FP Rate Precision Recall F-Measure ROC Area Class
1 0 1 1 1 1 Iris-
setosa
0.98 0.02 0.961 0.98 0.97 0.99 Iris-
versicolor
0.96 0.01 0.98 0.96 0.97 0.99 Iris-
virginica
Weighted Avg. 0.98 0.01 0.98 0.98 0.98 0.993
...
How costly is the metadata generation process?
3

How costly is the metadata generation process?
4

Reproducible Research - in theory
1.“Data/Metadata publicly available”
2.“The computer code and all the computational
procedures should be available”
3.“Ideally the computer code will encompass all
of the steps of computational analysis”
Dr. Peng / Dr. Jeff Leek
5

Reproducible Research - in practice
- Experiments are hard to reproduce, when not
impossible.
6

ML/DM Environments
7
LIBSVM
OpenML
IDEsFrameworks
Workflow Systems
Collaborative Env.

Machine Learning Frameworks
8
Platform Advantage Drawbacks
MLF Front-end No (High)
Interoperability
No/low updates
delay
No much code
flexibility
(Low) Workflow
Management
LIBSVM
Frameworks

Workflow Systems / Collaborative Environments
9
WFS High
Provenance
No (High)
Interoperability
Interoperabili
ty (*)
Updates are tool
dependent
Workflow
Management
No much code
flexibility
OpenML
Workflow Systems
Collaborative
Env.

Integrated Development Environments / Libraries
10
IDE/MLL High code-
flexibility
Low Provenance
No learning
curve
Low
Interoperability
Data Management
costs more
IDEs

Integrated Development Environments / Libraries
11
IDE/MLL High code-
flexibility
Low Provenance
No learning
curve
Low
Interoperability
Data Management
costs more
IDEs
motivation

Research Question
1.How to export machine learning variables -
incomes/outcomes (IDEs)?
- Architecture
- Schema
2.What is the existing approach that
minimizes the coding effort (IDEs)?
12

Existing solutions
13
Solution Advantage Drawbacks
stdout No Extra Coding
Effort Required
Lack of Provenance
Lack of Interoperability
Lack of Data Query Feature
DBMS Data Query
Feature
Extra Coding Effort
(Integration)
Lack of Provenance
Self-schema
Definition
Straightforward
Solution
Extra Coding Effort
Extra Analysis Effort
(modeling)
Lack of Provenance
IDEs

MEX Interfaces: from annotations to semantic metadata
14
Method Advantage Drawbacks
MEX
Interfaces
- Provenance
- Interoperabili
ty
- Data Query
Feature
- Automatic
Metadata
Generation
- Extra
Processing
Time
- Security
Issues (due
to
reflection)
IDEs

MEX Interfaces: from annotations to semantic metadata
1. Allow metadata
generation regardless of
the IDE, machine-
learning library and
context of the
experiment
15

- Generates metadata based on one of the state-of-the-
art vocabularies for machine learning
:exp_cf_1_2025644708_exe_2_algo a mexalgo:Algorithm ;
rdfs:label "Support Vector Machines" ;
mexalgo:hasAlgorithmClass mexalgo:SupportVectorMachines ;
mexalgo:hasHyperParameter :exp_cf_1_2025644708_exe_2_hyperpar_4, :exp_
cf_1_2025644708_exe_2_hyperpar_3, :exp_cf_1_2025644708_exe_2_hyperpar_2,
:exp_cf_1_2025644708_exe_2_hyperpar_1 ;
dct:identifier "svm".
...
MEX Vocabulary
16

Vocabularies and Ontologies for ML/DM
17
MEX
Onto-DM
DMOP
Exposé
W3C ML-Schema
Community
Group
https://www.w3.org/community/ml-
schema/ KDD
MLS

Vocabularies and Ontologies for ML/DM: MEX
18
Agnieszka Lawrynowicz et al. “The
Algorithm-Implementation-Execution
Ontology Design Pattern”, WOP2016.
Algorithm ⊑ InformationEntity
Implementation ⊑ InformationEntity
Implementation ⊑ ∃implements.Algorithm
Implementation ⊑
∃hasParameter.Parameter Execution ⊑
Process
Execution ⊑ ∃hasInput.ParameterSetting
Execution ⊑ ∃realizes.Algorithm
...
is a lightweight and flexible schema
for machine learning, based on PROV-
O
PROV-O: The PROV
Ontology
http://mex.aksw.org
https://www.w3.org/TR/prov-o/

- Reduces the overall coding effort
- No wrappers/adaptors/connectors do DBMS
- No schema development required
- Transparent process
MEX Interfaces: Features
19

- Transparent process (annotations and reflection)
Features: MEX Annotations
20
@ExperimentInfo(identifier = "e1", createdBy = "Esteves", email = "esteves@informatik.uni-
leipzig.de", title = "Weka Lib Example", tags = {"WEKA","J48", "DecisionTable", "MEX",
"Iris"})
@Hardware(cpu = MEXEnum.EnumProcessors.INTEL_COREI7, memory = MEXEnum.EnumRAM.SIZE_8GB,
hdType = "SSD")
@SamplingMethod(klass = MEXEnum.EnumSamplingMethods.CROSS_VALIDATION, trainSize = 0.5,
testSize = 0.5, folds = 10)
@InterfaceVersion(version = MEXEnum.EnumAnnotationInterfaceStyles.M1)
public class WekaExample001 { … }
java -cp /home/mexframework org.aksw.mex.framework.MetaGeneration -uc IrisWekaExample.java -out
mymex01.ttl

21
@Algorithm(algorithmClass = MEXEnum.EnumAlgorithmsClasses.J48, algorithmID = "1",
algorithmName = "J48", algorithmURI = "http://weka.sourceforge.net/doc.dev/weka/
classifiers/trees/J48.html")
public J48 wekaJ48;
@Algorithm(algorithmClass = MEXEnum.EnumAlgorithmsClasses.PART, algorithmID = "2",
algorithmName = "PART", algorithmURI = "http://weka.sourceforge.net/doc.dev/weka/
classifiers/rules/PART.html")
public PART wekaPART;
@DatasetName public String ds = "iris.arff"; Instances data; ...
mymex01.ttl

mymex01.ttl
@Measure(idMeasure = MEXEnum.EnumMeasures.ERROR) public List<Double> errors;
@Measure(idMeasure = MEXEnum.EnumMeasures.ACCURACY) public List<Double> accuracies;
@Start
public void myMainMethod(){ throws Exception {...}
22

MEX Annotations Log
23
Starting the process: MetaGeneration -uc interfaces.WekaExample001 -out mymex01.ttl
[main] INFO org.aksw.mex.interfaces.MetaGeneration - ********************** MEX Interfaces **********************
[main] INFO org.aksw.mex.interfaces.MetaGeneration - http://mex.aksw.org
[main] INFO org.aksw.mex.interfaces.MetaGeneration - Starting the meta annotation for class named: WekaExample001
[main] INFO org.aksw.mex.interfaces.MetaGeneration - @ExperimentInfo - OK
[main] INFO org.aksw.mex.interfaces.MetaGeneration - @Hardware - OK
[main] INFO org.aksw.mex.interfaces.MetaGeneration - @SamplingMethod - OK
[main] INFO org.aksw.mex.interfaces.MetaGeneration - invoking the main method: start
[main] INFO interfaces.WekaExample001 - Accuracy of J48: 94.00% - Error: 6.00%
[main] INFO interfaces.WekaExample001 - Accuracy of PART: 90.67% - Error: 9.33%
[main] INFO interfaces.WekaExample001 - Accuracy of DecisionTable: 92.67% - Error: 7.33%
[main] INFO interfaces.WekaExample001 - Accuracy of DecisionStump: 36.67% - Error: 63.33%
[main] INFO org.aksw.mex.interfaces.MetaGeneration - invoking the features method: getFeatures
[main] INFO org.aksw.mex.interfaces.MetaGeneration - @DataSet - OK
[main] INFO org.aksw.mex.interfaces.MetaGeneration - @Algorithm - OK
[main] INFO org.aksw.mex.interfaces.MetaGeneration - :: starting to add executions...
[main] INFO org.aksw.mex.interfaces.MetaGeneration - :: nr. executions = 4
[main] INFO org.aksw.mex.interfaces.MetaGeneration - :: idExecution = C1_MEX_EXEC_D1
[main] INFO org.aksw.mex.interfaces.MetaGeneration - error of Execution C1_MEX_EXEC_D1 : 6.0
[main] INFO org.aksw.mex.interfaces.MetaGeneration - accuracy of Execution C1_MEX_EXEC_D1 : 94.0
[main] WARN org.aksw.mex.log4mex.MEXSerializer - No model defined
[main] WARN org.aksw.mex.log4mex.MEXSerializer - No tool defined
[main] WARN org.aksw.mex.log4mex.MEXSerializer - No tool parameter defined
[main] INFO org.aksw.mex.interfaces.MetaGeneration - The MEX file has been successfully created: share it ;-)
[main] INFO org.aksw.mex.interfaces.MetaGeneration - process execution time (s): 1

Metadata file example
:hardware a
mexcore:HardwareConfiguration,
prov:Entity ;
mexcore:cpu "Intel Core
i7" ;
mexcore:hd "SSD" ;
mexcore:memory "8 GB" .
:execution_3 a
mexcore:OverallExecution,
mexcore:group "true" ;
prov:id "3" ;
prov:used this:phaseTEST ;
prov:wasInformedBy
this:configuration1 .
...
:sampling a prov:Entity ,
mexcore:CrossValidation ;
mexcore:folds "10" ;
mexcore:sequential "true";
mexcore:testSize "0.5" ;
mexcore:trainSize "0.5" .
:feature4 a mexcore:Feature,
prov:Entity ; rdfs:label
"petalwidth" ;
dct:identifier "4" .
:measure3_1 a
mexperf:StatisticalMeasure;
mexperf:error "7.333333333333329"
;
prov:wasInformedBy
this:execution_3 .
...
Features: Output file sample
24

Features: Logging Library
- Transparent process (logging)
25
MyMEX mex = new MyMEX();
try{
mex.setAuthorName("D Esteves");
mex.setAuthorEmail("esteves@informatik.uni-leipzig.de");
mex.setOrganization("Leipzig University");
mex.setExperimentId("E001");
mex.setExperimentTitle("my first experiment");
… }

- Transparent process (logging)
try{ MEXSerializer.getInstance().saveToDisk("./metafiles/log4mex/
ex003", "http://mex.aksw.org/examples/", mex,
MEXConstant.EnumRDFFormats.TTL);
}catch (Exception e){
System.out.print(e.toString());}
Features: Logging Library
26
...
mex.Configuration().addExecution(EnumExecutionsType.OVERALL,
EnumPhases.TRAIN);
mex.Configuration().Execution(ex1).setAlgorithm(alg1); 
mex.Configuration().Execution(ex1).addPerformance(EnumMeasures.ACCUR
ACY, x);
...

MEX Interfaces output x Weka default output
Features: Output file sample
27
=== Evaluation on training set ===
=== Summary ===
Correctly Classified Instances 147
98%
Incorrectly Classified Instances 3
2%
Kappa statistic 0.97
Mean absolute error
0.0233
Root mean squared error
0.108
Relative absolute error
5.2482%
Root relative squared error
22.9089%
Total Number of Instances 150
this:m11 a prov:Entity, mexperf:PerformanceMeasure;
dct:identifier "WekaPerformances";
mexperf:accuracy "0.9768"^^xsd:float;
mexperf:truePositive "147"^^xsd:integer;
mexperf:falsePositive "3"^^xsd:integer;
mexperf:kappaStatistics "0.97"^^xsd:float;
mexperf:meanAbsoluteError "0.0233"^^xsd:float;
mexperf:rootMeanSquaredError "0.108"^^xsd:float;
mexperf:relativeAbsoluteError
"0.052482"^^xsd:float;
mexperf:rootRelativeSquaredError
"0.0229089"^^xsd:float;
prov:wasGeneratedBy this:ep1;.
...

Features: Data Visualization (http://lodview.it/)
28

Features: Data Management “for free”
29
- ML Model that takes 3 days to be executed
- Iterates 300 times
- Produces/Has 5000 outcomes/incomes
- ….

Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016 30
- What are the top 4 configurations?

31
PREFIX mexcore: <http://mex.aksw.org/mex-core#> 
PREFIX mexperf: <http://mex.aksw.org/mex-perf#> 
PREFIX mexalgo: <http://mex.aksw.org/mex-algo#> 
PREFIX prov: <http://www.w3.org/ns/prov#> 
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#> 
SELECT DISTINCT ?ExecutionID ?Algorithm ?Performance ?fMeasure WHERE { 
?execution prov:used ?alg; prov:id ?ExecutionID. 
?Performance prov:wasGeneratedBy ?execution. 
?Performance mexperf:f1Measure ?fMeasure. 
?alg a mexalgo:Algorithm. 
?alg rdfs:label ?Algorithm. 
}  
ORDER BY DESC (?fMeasure) 
LIMIT 4

32
?ExecutionID ?Algorithm ?Performance ?fMeasure
"C0_MEX_EXEC_D44" "BaggingJ48" mea_clas_C0_MEX_EXEC_D44_cf_1_-568657719 0.9968
"C0_MEX_EXEC_D24" "Logistic
Model Trees"
mea_clas_C0_MEX_EXEC_D24_cf_1_-568657719 0.9952
"C0_MEX_EXEC_D16" "Random
Forest"
"C0_MEX_EXEC_D64" "Multilayer
Perceptron"

33

Conclusions and Future Work
Machine Learning Metadata Generation
- Generating high quality metadata is not a straightforward process
- Dealing with different outputs is not time-efficient
RQ1/RQ2 -> New methodology
- To automatize the process metadata generation for IDEs
- Data Management
- Based on one of the state-of-the-art vocabularies
Future Work
- Integrate others ML ontologies (ML-Schema)
- Analyse the coverage of the methodology with more machine learning
scenarios
- To create a more robust framework (e.g.: automatic pipelines based on
configuration files)
34

Conclusions and Future Work
Thank you!
Questions?
mex.aksw.org
35

MEX Interfaces: Automating Machine Learning Metadata Generation

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