modelling multi-component predictive systems as petri nets
TRANSCRIPT
Modelling Multi-Component Predictive Systems as Petri Nets
Manuel Martín Salvador, Marcin Budka, Bogdan GabrysBournemouth University, UK
{msalvador, mbudka, bgabrys}@bournemouth.ac.uk
ISC’2017Warsaw, PolandMay 31st, 2017
Outline1. Motivation2. Proposal3. Automation4. Experiments5. Conclusion and future work
MOTIVATION
Predictive modelling
LabelledData
SupervisedLearning
Algorithm
PredictiveModel
Predictive systems in the industryFault detection
Online prediction of hard-to-measure variables
Process monitoring
Demand forecasting
Classification based on computer vision
Picture is Creative Commons by Jm3
Need of preprocessingGarbage in, garbage out
Missing data
Outliers
High dimensionality
Normalisation
Lack of preprocessing can lead to inconsistent models
Multi-Component Predictive Systems
Data Postprocessing PredictionsPreprocessing Predictive Model
Multi-Component Predictive Systems
Data
Predictive Model
Postprocessing PredictionsPreprocessing Predictive Model
Predictive Model
Multi-Component Predictive Systems
Preprocessing
Data
Predictive Model
Postprocessing Predictions
Preprocessing
Preprocessing Predictive Model
Predictive Model
Requirements in the industryReliability - to provide truthful results
Robustness - to work under any circumstances or inconvenience
Transparency - to be comprehensible by human experts
Reproducibility - to replicate the results of an study
Low maintenance cost - to keep the system up-to-date at low cost
PROPOSAL
● Function composition: Difficult to model parallel paths. Can’t representate states of a system.
● Directed Acyclic Graph: Not enough to model process state or temporal behaviour..
● Petri net: Very flexible and robust mathematical background.
Expr
essi
ve p
ower
Y = h(g(f(X)))
f g hX Y
f g hX Y
How to model MCPS?
Mathematical modelling language invented in 1939 by Carl Adam Petri
token
place
transition
arc
N = (P,T,F)
Petri net
Example of Petri net
Reception Waiting Room
Check in
Consulting Room
Exit
Call in
Examination and diagnosis
Patient
Example of Petri net
Reception Waiting Room
Check in
Consulting Room
Exit
Call in
Examination and diagnosis
Example of Petri net
Reception Waiting Room
Check in
Consulting Room
Exit
Call in
Examination and diagnosis
Example of Petri net
Reception Waiting Room
Check in
Consulting Room
Exit
Call in
Examination and diagnosis
Example of Petri net
Reception Waiting Room
Check in
Consulting Room
Exit
Call in
Examination and diagnosis
Example of Petri net
Reception Waiting Room
Check in
Consulting Room
Exit
Call in
Examination and diagnosis
Example of Petri net
Reception Waiting Room
Check in
Consulting Room
Exit
Call in
Examination and diagnosis
Example of Petri net
Reception Waiting Room
Check in
Consulting Room
Exit
Call in
Examination and diagnosis
A Petri net is an MCPS iff all the following conditions apply:
● The Petri net is a WRI-WF-net● The places P\{i,o} have only a single input and a single output.● The Petri net is 1-bounded.● The Petri net is 1-sound.● The Petri net is ordinary.● All the transitions with multiple inputs or outputs are AND-join or AND-split,
respectively.● Any token is a tensor (i.e. multidimensional array)
Modelling MCPS as Petri net
Example of MCPS
Classifier
o
Replace missing values
Dimensionality reduction
Outlier handling
token(data) i
place
transition
MCPS = (P, Tλ, F)
AUTOMATION
Manual● WEKA● RapidMiner● Knime● IBM SPSS
Automatic● Auto-WEKA (Bayesian optimisation)● Auto-sklearn (Bayesian optimisation + Meta-learning)● TPOT (Genetic programming)● e-Lico IDA (Ontologies + Planning)
Example of WEKA workflow
MCPS composition
What are the best algorithms to process my data?
Algorithm Selection
How to tune the hyperparameters to get the best performance?
Hyperparameter Optimisation
Combined Algorithm Selection and Hyperparameter configuration problem
k-fold cross validation
Objective function(e.g. classification error)
HyperparametersMCPSs
Training dataset
Validation dataset
Thornton, C., Hutter, F., Hoos, H.H., Leyton-Brown, K.: Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms.In: Proc. of the 19th ACM SIGKDD. (2013) 847–855Martin Salvador M., Budka M., Gabrys B.: Automatic composition and optimisation of multicomponent predictive systems. IEEE Transactions on Automation Science and Engineering. under review - preprint available at https://arxiv.org/abs/1612.08789
CASH problem for MCPS
WEKA methods as search space
One-click black boxData + Time Budget → MCPS
Our contribution● Recursive extension of complex
hyperparameters in the search space.● Composition and optimisation of
MCPSs (including WEKA filters, predictors and meta-predictors)
● Petri net output as PNML format
Open-source. Download at:https://github.com/dsibournemouth/autoweka
Auto-WEKA for MCPS
WoPeD: Workflow Petri Net Designer
Open-source. Download:http://woped.dhbw-karlsruhe.de
Edit, analyze and simulate PNs
Load and save PNML
EXPERIMENTS
Building soft sensors for process industryTask: build a soft sensor to predict continuous values (regression)
7 datasets from real chemical production processes
70% training and optimisation, 30% testing
Auto-WEKA: 25 runs for 30 hours with different seeds, keep the best.
Optimisation measure: RMSE
Baseline: 4 most popular methods for soft sensors (PCR, PLS, MLP and RBF)
dataset RMSE of best (test)
Difference with baseline
absorber 0.8989 ↑ 0.0844
catalyst 0.0736 ↑ 0.1144
debutanizer 0.1745 ↓ 0.0035
drier 1.3744 ↑ 0.0573
oxeno 0.0226 ↑ 0.0042
sulfur 0.0366 ↑ 0.0030
thermalox 0.6904 ↑ 0.6170
● Data distribution can change over time and affect predictions○ External factors (e.g. weather conditions, new regulations)○ Internal factors (e.g. quality of materials, equipment deterioration)
Source: INFER project
Maintaining an MCPS
GFMMZ-Score PCA Min-Max
Tim
e
i p1 p2 p3 o
data
meta-dataprediction
[-3.1, 2.7]
x1 = 3.6
[-3.1, 3.6]
Reactive adaptation of MCPS
Conclusion and future workAutomatic composition of MCPS can speed up the process of building predictive systems though can end up being a black-box process
Representing MCPSs as Petri nets has a number of benefits:
● Increase transparency● Verification● Vendor-independent
Future work:
● Workflow algebra to model MCPSs adaptation● Timed Petri nets to model task duration and delays
THANKS!
Paper: http://bit.ly/mcps-petri-nets
Slides: http://www.slideshare.net/draxus
Manuel <[email protected]>
@draxus