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scikit
machine learning in Python
ni
Scikit-learn & nilearn
Democratisation of machine learning for brain imaging
Gaël Varoquaux
scikit
machine learning in Python
ni
Scikit-learn & nilearn
Democratisation of machine learning for brain imaging
Gaël Varoquaux
Outline:
1 Machine learning for brain imaging
2 Scikit-learn
3 Nilearn
1 Machine learning for brain
imaging
Focus on brain mapping
G Varoquaux 2
1 Predictive models in medical applications
G Varoquaux 3
1 Predictive models in medical applications
Diagnosis: finding the nature of a disease condition
Pronosis: predicting the evolution
⇒ Therapeutic indications
Early biomarkers: detection before standard symptoms
⇒ Population screening
Quantitative biomarkers: to follow disease progression
⇒ drug development
G Varoquaux 3
1 Predictive models in medical applications
Cannot replace the physician:
Patient history
Therapeutic strategies subject to logistics
...
⇒ No black-box
Segmentation, denoising task
as much as prediction
The why question
G Varoquaux 3
Understanding brain function
Cognitive neuroimaging:
from neural activity to thoughts
G Varoquaux 4
[Varoquaux and Thirion 2014] How machine learning is
shaping cognitive neuroimaging
Cognitive neuroimaging and machine learning
G Varoquaux 5
Cognitive neuroimaging and machine learning
Predicting neural response: encoding
G Varoquaux 5
Cognitive neuroimaging and machine learning
Predicting the task: decoding
G Varoquaux 5
Cognitive neuroimaging and machine learning
Unsupervised learning on brain activity
G Varoquaux 5
[Varoquaux and Thirion 2014] How machine learning is
shaping cognitive neuroimaging
Cognitive neuroimaging and machine learning
Forward Inference
Encoding
Mass-Univariate standard analysis
Reverse Inference
Decoding
Multi-voxel pattern analysis
ICA & linear decompositions
Brain parcellations
Resting state
Descriptions of behavior
Measurements
of brain activity
Brain maps
Cognitive concepts
G Varoquaux 5
1 Decoding: linear models on brain maps
Design
matrix
× Coefficients =
Coefficients are
brain maps
Target
G Varoquaux 6
1 Unsupervised mapping: resting state
Data without labels is cheaper & universal
But often without salient features
(as in rest fMRI)
G Varoquaux 7
1 Unsupervised mapping: resting state
Define functional regions
G Varoquaux 7
1 Unsupervised mapping: resting state
Define functional regions
Learn interactions
G Varoquaux 7
1 Unsupervised mapping: resting state
Define functional regions
Learn interactions
Detect differences
G Varoquaux 7
1 From rest to biomarkers
Functional
connectivity
matrix
Time series
extraction
Region
definition
Supervised learning
RS-fMRI
G Varoquaux 8
2 Scikit-learn
scikit
machine learning in Python
G Varoquaux 9
2 scikit-learn’s vision: Machine learning for everyone
Outreach
across scientific fields,
applications, communities
Enabling
foster innovation
G Varoquaux 10
2 A Python library
Python
High-level language, for users and developers
General-purpose: suitable for any application
Excellent interactive use
Web searches: Google trends
G Varoquaux 11
2 User base
350 000 returning users 8 000 citations
Employer
Industry Academia
Other
63%
3%
34%
G Varoquaux 12
2 Tradeoffs for outreach
Algorithms and models with good failure mode
Avoid parameters hard to set or fragile convergence
Statistical computing = ill-posed & data-dependent
Didactic documentation
Course on machine learning
Rich examples
G Varoquaux 13
2 Machine learning without learning the machinery
A library, not a program
More expressive and flexible
Easy to include in an ecosystem
G Varoquaux 14
2 Machine learning without learning the machinery
A library, not a program
More expressive and flexible
Easy to include in an ecosystem
Ease of use
Machine learning in new places = innovation
Great API, greats docs
G Varoquaux 14
2 API:
The greybox model
Building bricks
to combine with domain-specific knowledge
interchangeable (mostly)
G Varoquaux 15
2 API:
The greybox model
from s k l e a r n import svm
c l a s s i f i e r = svm.SVC()
c l a s s i f i e r . f i t ( X_train , Y_train )
Y_test = c l a s s i f i e r . p r e d i c t ( X_test )
# or
X_red = c l a s s i f i e r . t r a n s f o r m ( X_test )
Access to the model’s inner parameters
c o e f = c l a s s i f i e r . coef_
G Varoquaux 15
2 Very rich feature set: 160 estimators
Supervised learning
Decision trees (Random-Forest, Boosted Tree)
Linear models SVM
Gaussian processes ...
Unsupervised Learning
Clustering Mixture models
Dictionary learning ICA
Outlier detection ...
Model selection
Cross-validation
Parameter optimization
G Varoquaux 16
2 Models most used in scikit-learn
1. Logistic regression, SVM
2. Random forests
3. PCA
4. Kmeans
5. Naive Bayes
6. Nearest neighbors
From access statistics on the website
G Varoquaux 17
More gems in scikit-learn
SAGA:
linear_model.LogisticRegression(solver=’saga’)
Fast linear model on biggish data
G Varoquaux 18
More gems in scikit-learn
SAGA:
linear_model.LogisticRegression(solver=’saga’)
Fast linear model on biggish data
PCA == RandomizedPCA: (0.18)
Heuristic to switch PCA to random linear algebra
Fights global warming
Huge speed gains for biggish data
G Varoquaux 18
2 Community-based development in scikit-learn
2010 2012 2014 2016
0
25
50
Huge feature set: benefits of a large team
Monthly contributors
More than 700 contributors
∼ 20 core contributors
https://www.openhub.net/p/scikit-learn
Community-driven project
G Varoquaux 19
2 Scikit-learn-contrib
Scaling the scikit-learn universe quicker
https://github.com/scikit-learn-contrib
py-earth multivariate adaptive regression splines
imbalanced-learn under-sampling and over-sampling
lightning fast linear models
polylearn factorization machines and polynomial networks
hdbscan high-performance clustering
forest-confidence-interval confidence interval for forests
boruta_py boruta feature selection
Much more libraries outside scikit-learn universe
G Varoquaux 20
3 Nilearn
ni
G Varoquaux 21
3 Commoditizing science
Visual image reconstruction from human brain activity
[Miyawaki, et al. (2008)]
“brain reading”
G Varoquaux 22
3 Commoditizing science
Visual image reconstruction from human brain activity
[Miyawaki, et al. (2008)]
Make it work, make it right, make it boring
3 Commoditizing science
Visual image reconstruction from human brain activity
[Miyawaki, et al. (2008)]
Make it work, make it right, make it boring
http://nilearn.github.io/auto_examples/
plot_miyawaki_reconstruction.html
Readable, simple,
reproduction of results
ni
http://nilearn.github.ioG Varoquaux 22
3 Challenges we have to solve
Make using scikit-learn on neuroimaging easy
Getting the data
Struggle for open data
Massaging the data for machine-learning
Very simple signal processing
Documentation
Users do not know what they need
Output + visualization of results
Putting it in application terms
G Varoquaux 23
3 Nilearn in practice
Getting the data
f i l e s = d a t a s e t s . fetch_haxby ()
Caching of the downloads
Resume of partial downloads
G Varoquaux 24
3 Nilearn in practice
Getting the data
f i l e s = d a t a s e t s . fetch_haxby ()
Massaging the data for machine-learning
masker = N i f t i M a s k e r (mask_img=’mask.nii’,
s t a n d a r d i z e = True )
data = masker . f i t _ t r a n s f o r m (’fmri.nii’)
Filenames to data matrix (memory-efficient I/O)
Common preprocessing steps included
G Varoquaux 24
3 Nilearn in practice
Getting the data
f i l e s = d a t a s e t s . fetch_haxby ()
Massaging the data for machine-learning
masker = N i f t i M a s k e r (mask_img=’mask.nii’,
s t a n d a r d i z e = True )
data = masker . f i t _ t r a n s f o r m (’fmri.nii’)
Learning with scikit-learn
e s t i m a t o r . f i t ( data , l a b e l s )
That’s easy!
G Varoquaux 24
3 Nilearn in practice
Getting the data
f i l e s = d a t a s e t s . fetch_haxby ()
Massaging the data for machine-learning
masker = N i f t i M a s k e r (mask_img=’mask.nii’,
s t a n d a r d i z e = True )
data = masker . f i t _ t r a n s f o r m (’fmri.nii’)
Learning with scikit-learn
e s t i m a t o r . f i t ( data , l a b e l s )
Output
plot_stat_map ( masker . i n v e r s e _ t r a n s f o r m (
e s t i m a t o r . weights_ ))
G Varoquaux 24
3 There is more
Learners taylored to statistics of brain maps
Image penalties on linear models
SVM sparse TV- 1
Total-variation penalization
Impose sparsity on the
gradient of the image
In fMRI: [Michel... 2011]
nilearn.github.io/auto_examples/02_decoding/
plot_haxby_space_net.htmlG Varoquaux 25
3 There is more
Learners taylored to statistics of brain maps
Image penalties on linear models
Unsupervised dictionary-learning
Brain regions from rest-fMRI
nilearn.github.io/auto_examples/03_connectivity/
plot_compare_resting_state_decomposition.htmlG Varoquaux 25
3 There is more
Learners taylored to statistics of brain maps
Image penalties on linear models
Unsupervised dictionary-learning
Brain regions from rest-fMRI
Connectome pipeline:
extraction and supervised learning
nilearn.github.io/auto_examples/03_connectivity/
plot_multi_subject_connectome.html
G Varoquaux 25
scikit
machine learning in Python
ni
@GaelVaroquaux
Democratisation of machine learning for an application
Generic set of robust algorithms
Foster innovation
scikit
machine learning in Python
ni
@GaelVaroquaux
Democratisation of machine learning for an application
Generic set of robust algorithms
For the application: I/O, visualization & open data
Complete, runnable examples
Solve day-to-day problem
Create interest
scikit
machine learning in Python
ni
@GaelVaroquaux
Democratisation of machine learning for an application
Generic set of robust algorithms
For the application: I/O, visualization & open data
Documentation, API, ease of installation
Lower the bar
scikit
machine learning in Python
ni
@GaelVaroquaux
Democratisation of machine learning for an application
Generic set of robust algorithms
For the application: I/O, visualization & open data
Documentation, API, ease of installation
References I
V. Michel, A. Gramfort, G. Varoquaux, E. Eger, and B. Thirion.
Total variation regularization for fMRI-based prediction of
behavior. Medical Imaging, IEEE Transactions on, 30:1328,
2011.
G. Varoquaux and B. Thirion. How machine learning is shaping
cognitive neuroimaging. GigaScience, 3:28, 2014.

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Scikit-learn and nilearn: Democratisation of machine learning for brain imaging

  • 1. scikit machine learning in Python ni Scikit-learn & nilearn Democratisation of machine learning for brain imaging Gaël Varoquaux
  • 2. scikit machine learning in Python ni Scikit-learn & nilearn Democratisation of machine learning for brain imaging Gaël Varoquaux Outline: 1 Machine learning for brain imaging 2 Scikit-learn 3 Nilearn
  • 3. 1 Machine learning for brain imaging Focus on brain mapping G Varoquaux 2
  • 4. 1 Predictive models in medical applications G Varoquaux 3
  • 5. 1 Predictive models in medical applications Diagnosis: finding the nature of a disease condition Pronosis: predicting the evolution ⇒ Therapeutic indications Early biomarkers: detection before standard symptoms ⇒ Population screening Quantitative biomarkers: to follow disease progression ⇒ drug development G Varoquaux 3
  • 6. 1 Predictive models in medical applications Cannot replace the physician: Patient history Therapeutic strategies subject to logistics ... ⇒ No black-box Segmentation, denoising task as much as prediction The why question G Varoquaux 3
  • 7. Understanding brain function Cognitive neuroimaging: from neural activity to thoughts G Varoquaux 4
  • 8. [Varoquaux and Thirion 2014] How machine learning is shaping cognitive neuroimaging Cognitive neuroimaging and machine learning G Varoquaux 5
  • 9. Cognitive neuroimaging and machine learning Predicting neural response: encoding G Varoquaux 5
  • 10. Cognitive neuroimaging and machine learning Predicting the task: decoding G Varoquaux 5
  • 11. Cognitive neuroimaging and machine learning Unsupervised learning on brain activity G Varoquaux 5
  • 12. [Varoquaux and Thirion 2014] How machine learning is shaping cognitive neuroimaging Cognitive neuroimaging and machine learning Forward Inference Encoding Mass-Univariate standard analysis Reverse Inference Decoding Multi-voxel pattern analysis ICA & linear decompositions Brain parcellations Resting state Descriptions of behavior Measurements of brain activity Brain maps Cognitive concepts G Varoquaux 5
  • 13. 1 Decoding: linear models on brain maps Design matrix × Coefficients = Coefficients are brain maps Target G Varoquaux 6
  • 14. 1 Unsupervised mapping: resting state Data without labels is cheaper & universal But often without salient features (as in rest fMRI) G Varoquaux 7
  • 15. 1 Unsupervised mapping: resting state Define functional regions G Varoquaux 7
  • 16. 1 Unsupervised mapping: resting state Define functional regions Learn interactions G Varoquaux 7
  • 17. 1 Unsupervised mapping: resting state Define functional regions Learn interactions Detect differences G Varoquaux 7
  • 18. 1 From rest to biomarkers Functional connectivity matrix Time series extraction Region definition Supervised learning RS-fMRI G Varoquaux 8
  • 19. 2 Scikit-learn scikit machine learning in Python G Varoquaux 9
  • 20. 2 scikit-learn’s vision: Machine learning for everyone Outreach across scientific fields, applications, communities Enabling foster innovation G Varoquaux 10
  • 21. 2 A Python library Python High-level language, for users and developers General-purpose: suitable for any application Excellent interactive use Web searches: Google trends G Varoquaux 11
  • 22. 2 User base 350 000 returning users 8 000 citations Employer Industry Academia Other 63% 3% 34% G Varoquaux 12
  • 23. 2 Tradeoffs for outreach Algorithms and models with good failure mode Avoid parameters hard to set or fragile convergence Statistical computing = ill-posed & data-dependent Didactic documentation Course on machine learning Rich examples G Varoquaux 13
  • 24. 2 Machine learning without learning the machinery A library, not a program More expressive and flexible Easy to include in an ecosystem G Varoquaux 14
  • 25. 2 Machine learning without learning the machinery A library, not a program More expressive and flexible Easy to include in an ecosystem Ease of use Machine learning in new places = innovation Great API, greats docs G Varoquaux 14
  • 26. 2 API: The greybox model Building bricks to combine with domain-specific knowledge interchangeable (mostly) G Varoquaux 15
  • 27. 2 API: The greybox model from s k l e a r n import svm c l a s s i f i e r = svm.SVC() c l a s s i f i e r . f i t ( X_train , Y_train ) Y_test = c l a s s i f i e r . p r e d i c t ( X_test ) # or X_red = c l a s s i f i e r . t r a n s f o r m ( X_test ) Access to the model’s inner parameters c o e f = c l a s s i f i e r . coef_ G Varoquaux 15
  • 28. 2 Very rich feature set: 160 estimators Supervised learning Decision trees (Random-Forest, Boosted Tree) Linear models SVM Gaussian processes ... Unsupervised Learning Clustering Mixture models Dictionary learning ICA Outlier detection ... Model selection Cross-validation Parameter optimization G Varoquaux 16
  • 29. 2 Models most used in scikit-learn 1. Logistic regression, SVM 2. Random forests 3. PCA 4. Kmeans 5. Naive Bayes 6. Nearest neighbors From access statistics on the website G Varoquaux 17
  • 30. More gems in scikit-learn SAGA: linear_model.LogisticRegression(solver=’saga’) Fast linear model on biggish data G Varoquaux 18
  • 31. More gems in scikit-learn SAGA: linear_model.LogisticRegression(solver=’saga’) Fast linear model on biggish data PCA == RandomizedPCA: (0.18) Heuristic to switch PCA to random linear algebra Fights global warming Huge speed gains for biggish data G Varoquaux 18
  • 32. 2 Community-based development in scikit-learn 2010 2012 2014 2016 0 25 50 Huge feature set: benefits of a large team Monthly contributors More than 700 contributors ∼ 20 core contributors https://www.openhub.net/p/scikit-learn Community-driven project G Varoquaux 19
  • 33. 2 Scikit-learn-contrib Scaling the scikit-learn universe quicker https://github.com/scikit-learn-contrib py-earth multivariate adaptive regression splines imbalanced-learn under-sampling and over-sampling lightning fast linear models polylearn factorization machines and polynomial networks hdbscan high-performance clustering forest-confidence-interval confidence interval for forests boruta_py boruta feature selection Much more libraries outside scikit-learn universe G Varoquaux 20
  • 35. 3 Commoditizing science Visual image reconstruction from human brain activity [Miyawaki, et al. (2008)] “brain reading” G Varoquaux 22
  • 36. 3 Commoditizing science Visual image reconstruction from human brain activity [Miyawaki, et al. (2008)] Make it work, make it right, make it boring
  • 37. 3 Commoditizing science Visual image reconstruction from human brain activity [Miyawaki, et al. (2008)] Make it work, make it right, make it boring http://nilearn.github.io/auto_examples/ plot_miyawaki_reconstruction.html Readable, simple, reproduction of results ni http://nilearn.github.ioG Varoquaux 22
  • 38. 3 Challenges we have to solve Make using scikit-learn on neuroimaging easy Getting the data Struggle for open data Massaging the data for machine-learning Very simple signal processing Documentation Users do not know what they need Output + visualization of results Putting it in application terms G Varoquaux 23
  • 39. 3 Nilearn in practice Getting the data f i l e s = d a t a s e t s . fetch_haxby () Caching of the downloads Resume of partial downloads G Varoquaux 24
  • 40. 3 Nilearn in practice Getting the data f i l e s = d a t a s e t s . fetch_haxby () Massaging the data for machine-learning masker = N i f t i M a s k e r (mask_img=’mask.nii’, s t a n d a r d i z e = True ) data = masker . f i t _ t r a n s f o r m (’fmri.nii’) Filenames to data matrix (memory-efficient I/O) Common preprocessing steps included G Varoquaux 24
  • 41. 3 Nilearn in practice Getting the data f i l e s = d a t a s e t s . fetch_haxby () Massaging the data for machine-learning masker = N i f t i M a s k e r (mask_img=’mask.nii’, s t a n d a r d i z e = True ) data = masker . f i t _ t r a n s f o r m (’fmri.nii’) Learning with scikit-learn e s t i m a t o r . f i t ( data , l a b e l s ) That’s easy! G Varoquaux 24
  • 42. 3 Nilearn in practice Getting the data f i l e s = d a t a s e t s . fetch_haxby () Massaging the data for machine-learning masker = N i f t i M a s k e r (mask_img=’mask.nii’, s t a n d a r d i z e = True ) data = masker . f i t _ t r a n s f o r m (’fmri.nii’) Learning with scikit-learn e s t i m a t o r . f i t ( data , l a b e l s ) Output plot_stat_map ( masker . i n v e r s e _ t r a n s f o r m ( e s t i m a t o r . weights_ )) G Varoquaux 24
  • 43. 3 There is more Learners taylored to statistics of brain maps Image penalties on linear models SVM sparse TV- 1 Total-variation penalization Impose sparsity on the gradient of the image In fMRI: [Michel... 2011] nilearn.github.io/auto_examples/02_decoding/ plot_haxby_space_net.htmlG Varoquaux 25
  • 44. 3 There is more Learners taylored to statistics of brain maps Image penalties on linear models Unsupervised dictionary-learning Brain regions from rest-fMRI nilearn.github.io/auto_examples/03_connectivity/ plot_compare_resting_state_decomposition.htmlG Varoquaux 25
  • 45. 3 There is more Learners taylored to statistics of brain maps Image penalties on linear models Unsupervised dictionary-learning Brain regions from rest-fMRI Connectome pipeline: extraction and supervised learning nilearn.github.io/auto_examples/03_connectivity/ plot_multi_subject_connectome.html G Varoquaux 25
  • 46. scikit machine learning in Python ni @GaelVaroquaux Democratisation of machine learning for an application Generic set of robust algorithms Foster innovation
  • 47. scikit machine learning in Python ni @GaelVaroquaux Democratisation of machine learning for an application Generic set of robust algorithms For the application: I/O, visualization & open data Complete, runnable examples Solve day-to-day problem Create interest
  • 48. scikit machine learning in Python ni @GaelVaroquaux Democratisation of machine learning for an application Generic set of robust algorithms For the application: I/O, visualization & open data Documentation, API, ease of installation Lower the bar
  • 49. scikit machine learning in Python ni @GaelVaroquaux Democratisation of machine learning for an application Generic set of robust algorithms For the application: I/O, visualization & open data Documentation, API, ease of installation
  • 50. References I V. Michel, A. Gramfort, G. Varoquaux, E. Eger, and B. Thirion. Total variation regularization for fMRI-based prediction of behavior. Medical Imaging, IEEE Transactions on, 30:1328, 2011. G. Varoquaux and B. Thirion. How machine learning is shaping cognitive neuroimaging. GigaScience, 3:28, 2014.