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Computational practices for reproducible science
Ga¨el Varoquaux
import science
science.reproduce()
Computational practices for reproducible science
Ga¨el Varoquaux
This talk:
High-level advice on code in science
Pointers ...
Computational practices for reproducible science
Ga¨el Varoquaux
Outline:
1 Coding for science, reproducibly
2 Making libr...
Where I come from
PhD in physics
Computers:
Matlab
Instrument control
Python for Science:
Startup guy
Mayavi
scikit-learn
...
1 Coding for science,
reproducibly
G Varoquaux 3
1 Computational reproducibility
Computational reproducibility:
Automate everything
Control the environment
G Varoquaux 4
1 Automate everything
Just a simple matter of programming
G Varoquaux 5
1 Automate everything
Just a simple matter of programming
Scientific workflow:
Based on intuition
and experimentation
⇒ Iter...
1 Automate everything
Just a simple matter of programming
Scientific workflow:
Based on intuition
and experimentation
⇒ Iter...
1 Automate everything
Just a simple matter of programming
Scientific workflow:
Based on intuition
and experimentation
⇒ Iter...
1 Reproducible computational science
Harder than it seemed
Impediments
Missing steps / files
Libraries have changed
Non por...
1 Reproducible computational science
Harder than it seemed
Impediments
Missing steps / files
Libraries have changed
Non por...
1 How I work progressive consolidation
Start with a script playing to understand the problem
G Varoquaux 10
1 How I work progressive consolidation
Start with a script playing to understand the problem
Identify blocks/operations ⇒ ...
1 How I work progressive consolidation
Start with a script playing to understand the problem
Identify blocks/operations ⇒ ...
1 How I work progressive consolidation
Start with a script playing to understand the problem
Identify blocks/operations ⇒ ...
1 How I work progressive consolidation
Start with a script playing to understand the problem
Identify blocks/operations ⇒ ...
1 Mitigating long compute times
by storing intermediate results
Makefiles
Accessible intermediate results
Break control flow...
1 Mitigating long compute times
by storing intermediate results
Makefiles
Accessible intermediate results
Break control flow...
1 A design pattern in computational experiments
MVC pattern (from Wikipedia
Model
Manages the data
and rules of the
applic...
1 A design pattern in computational experiments
MVC pattern (from Wikipedia
Model
Manages the data
and rules of the
applic...
1 A design pattern in computational experiments
MVC pattern (from Wikipedia
Model
Manages the data
and rules of the
applic...
1 A design pattern in computational experiments
MVC pattern (from Wikipedia
Model
Manages the data
and rules of the
applic...
Adopting software-engineering best practices
G Varoquaux 13
1 The ladder of code quality
Use static checking in your editor seriously
G Varoquaux 14
1 The ladder of code quality
Use static checking in your editor seriously
Coding convention, good naming
“Any fool can wri...
1 The ladder of code quality
Use static checking in your editor seriously
Coding convention, good naming
Version control U...
1 The ladder of code qualityIncreasingcost
?
Use static checking in your editor seriously
Coding convention, good naming
V...
1 The ladder of code qualityIncreasingcost
?
Use static checking in your editor seriously
Coding convention, good naming
V...
1 LibrariesIncreasingcost
?
Use static checking in your editor seriously
Coding convention, good naming
Version control Us...
1 LibrariesIncreasingcost
?
Use static checking in your editor seriously
Coding convention, good naming
Version control Us...
1 Libraries enable reproducible science
Reproducibility
Rerun and come to the same conclusion
An argument for copying all ...
1 Libraries enable reproducible science
Reproducibility
Rerun and come to the same conclusion
An argument for copying all ...
1 Libraries enable reproducible science
Reproducibility
Rerun and come to the same conclusion
An argument for copying all ...
1 Libraries enable reproducible science
Reproducibility
Rerun and come to the same conclusion
An argument for copying all ...
2 Making libraries
to enable reproducibility
G Varoquaux 17
2 Pick the right battles: project scope
Project idea
A software implementing:
i) machine learning
and ii) neuroimaging
and...
2 Pick the right battles: project scope
Project idea
A software implementing:
i) machine learning
and ii) neuroimaging
and...
2 Pick the right battles: project scope
Project idea
A software implementing:
i) machine learning
and ii) neuroimaging
and...
2 Pick the right battles: project scope
Project idea
A software implementing:
i) machine learning
and ii) neuroimaging
and...
2 Short cycles, limited ambitions
Keep coming back to your users
Release early, release often
G Varoquaux 19
2 Simplicity
Complexity increase superlinearly
[An Experiment on Unit Increase in Problem Complexity,
Woodfield 1979]
25% i...
2 Simplicity
Complexity increase superlinearly
[An Experiment on Unit Increase in Problem Complexity,
Woodfield 1979]
25% i...
2 Quality
Quality is free
This is a book, by Philip Crosby
G Varoquaux 21
2 You need quality
Quality will give you users
Bugs give you bad rap
Quality will give you developers
Contribute to learn ...
2 Quality: what & how
Great documentation
Simplify, but don’t dumb down
Focus on what the user is trying to solve
Great AP...
2 Quality: what & how
Great documentation
Simplify, but don’t dumb down
Focus on what the user is trying to solve
Great AP...
2 Quality assurance
Code review: pull requests
Can include newcomers
We read each others code
Everything is discussed:
- S...
2 Quality assurance
Unit testing
Everything is tested
Great for numerics
Overall tests enforce on all estimators
- consist...
2 Dependencies
Beyond installation, a challenge is to ensure package
versions play way together: correctness of the code
B...
2 Dependencies and their upgrade
It’s a fact: users hate upgrading
If it ain’t broken, don’t fix it
even if it is, apparent...
2 Declaring undependence?
Monolythic packages with no dependencies...
But:
Scaling is hard
Complexity grows as square of c...
3 From software back to science
G Varoquaux 29
3 Beyond computational reproducibility
Make every computational step reproducible,
and good science will emerge
G Varoquau...
3 Beyond computational reproducibility
Make every computational step reproducible,
and good science will emerge
Estimating...
In practice, the best way to improve research
is to use the right (conceptual) tools.
G Varoquaux 31
3 Managing complexity
In practice, the best way to improve research
is to use the right (conceptual) tools.
The everyday r...
3 Tools that reduce cognitive overload
It’s a design problem
G Varoquaux 33
3 Tools that reduce cognitive overload
Jonathan Ive, an industrial designer, is #4 at Apple
Code different.
G Varoquaux 34
3 Some API design principles for the scipy stack
Consistency, consistency, consistency
Functions are easier to understand ...
3 Some API design principles for the scipy stack
Consistency, consistency, consistency
np.save(file, obj) pickle.dump(obj,...
3 Some API design principles for the scipy stack
Consistency, consistency, consistency
Functions are easier to understand ...
3 Some API design principles for the scipy stack
Consistency, consistency, consistency
Functions are easier to understand ...
3 Some API design principles for the scipy stack
Consistency, consistency, consistency
Functions are easier to understand ...
3 Some API design principles for the scipy stack
Consistency, consistency, consistency
Functions are easier to understand ...
3 Some API design principles for the scipy stack
Consistency, consistency, consistency
Functions are easier to understand ...
3 Some API design principles for the scipy stack
Consistency, consistency, consistency
Functions are easier to understand ...
3 Some API design principles for the scipy stack
Consistency, consistency, consistency
Functions are easier to understand ...
3 Some API design principles for the scipy stack
Consistency, consistency, consistency
Functions are easier to understand ...
3 Some API design principles for the scipy stack
Consistency, consistency, consistency
Functions are easier to understand ...
3 Scikit-learn API
Scikit-learn cheat sheet
Scikit-learn
Fit and predict
>>> estimator = Estimator(param1=param1)
>>> esti...
3 Scikit-learn API
Scikit-learn cheat sheet
Scikit-learn
Fit and predict
>>> estimator = Estimator(param1=param1)
>>> esti...
3 Example-driven development
The 3-liner as the new cool
Teaching others
Teaching yourself
Write examples that solve end p...
3 Example-driven development
The 3-liner as the new cool
Teaching others
Teaching yourself
Write examples that solve end p...
3 Example-driven development
The 3-liner as the new cool
Teaching others
Teaching yourself
Write examples that solve end p...
3 Building great documentation
Focus on explaining concepts (hint: write plain English)
Less is more: prioritize, avoid re...
3 Reusable science
scikit-learn is the new machine-learning textbook
nilearn is the new neuroimaging review article
Experi...
3 Reusable science
scikit-learn is the new machine-learning textbook
nilearn is the new neuroimaging review article
Experi...
3 Reusable science
scikit-learn is the new machine-learning textbook
nilearn is the new neuroimaging review article
Experi...
3 Reusable science
scikit-learn is the new machine-learning textbook
nilearn is the new neuroimaging review article
Experi...
Coding as a scientist
Final code should be auditable,
ideally reusable
Tension between interactive computing
& automating
...
Coding as a scientist
Final code should be auditable,
ideally reusable
Tension between interactive computing
& automating
...
@GaelVaroquaux
Computational practices for reproducible science
Software-engineering skills are superpowers
“text-book” so...
@GaelVaroquaux
Computational practices for reproducible science
Software-engineering skills are superpowers
“text-book” so...
@GaelVaroquaux
Computational practices for reproducible science
Software-engineering skills are superpowers
“text-book” so...
@GaelVaroquaux
Computational practices for reproducible science
Software-engineering skills are superpowers
“text-book” so...
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Computational practices for reproducible science

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Reconciling bleeding-edge scientific results and reproducible research may seem a conundrum in our fast-paced high-pressure academic world. I discuss the practices that I found useful in computational work. At a high level, it is important to navigate the space between rapid experimentation and industrial-grade software development. I advocate adopting more and more software-engineering best practices as a project matures. I will also discuss how to turn the computational work into libraries, and to ensure the quality of the resulting libraries. And I conclude on how those libraries need to fit in the larger picture of the exercise of research to give better science.

Published in: Science

Computational practices for reproducible science

  1. 1. Computational practices for reproducible science Ga¨el Varoquaux import science science.reproduce()
  2. 2. Computational practices for reproducible science Ga¨el Varoquaux This talk: High-level advice on code in science Pointers to good software practices
  3. 3. Computational practices for reproducible science Ga¨el Varoquaux Outline: 1 Coding for science, reproducibly 2 Making libraries 3 From software back to science
  4. 4. Where I come from PhD in physics Computers: Matlab Instrument control Python for Science: Startup guy Mayavi scikit-learn joblib nilearn Applied maths / machine learning for neuroscience G Varoquaux 2
  5. 5. 1 Coding for science, reproducibly G Varoquaux 3
  6. 6. 1 Computational reproducibility Computational reproducibility: Automate everything Control the environment G Varoquaux 4
  7. 7. 1 Automate everything Just a simple matter of programming G Varoquaux 5
  8. 8. 1 Automate everything Just a simple matter of programming Scientific workflow: Based on intuition and experimentation ⇒ Iterative Conjecture Experiment G Varoquaux 6
  9. 9. 1 Automate everything Just a simple matter of programming Scientific workflow: Based on intuition and experimentation ⇒ Iterative Conjecture Experiment needs consolidation keeping flexibility G Varoquaux 7
  10. 10. 1 Automate everything Just a simple matter of programming Scientific workflow: Based on intuition and experimentation ⇒ Iterative Conjecture Experiment needs consolidation keeping flexibility Lost in iterations G Varoquaux 8
  11. 11. 1 Reproducible computational science Harder than it seemed Impediments Missing steps / files Libraries have changed Non portable code Statistical / numerical instabilities No one knows where the info is G Varoquaux 9
  12. 12. 1 Reproducible computational science Harder than it seemed Impediments Missing steps / files Libraries have changed Non portable code Statistical / numerical instabilities No one knows where the info is Technical Human Code quality matters Manual steps are evil G Varoquaux 9
  13. 13. 1 How I work progressive consolidation Start with a script playing to understand the problem G Varoquaux 10
  14. 14. 1 How I work progressive consolidation Start with a script playing to understand the problem Identify blocks/operations ⇒ move to a function Use functions Obstacle: local scope requires identifying input and output variables That’s a good thing Interactive debugging / understanding inside a function: %debug in IPython Functions are the basic reusable abstraction G Varoquaux 10
  15. 15. 1 How I work progressive consolidation Start with a script playing to understand the problem Identify blocks/operations ⇒ move to a function As they stabilize, move to a module Modules enable sharing between experiments ⇒ avoid 1000 lines scripts + commented code enable testing Fast experiments as tests ⇒ gives confidence, hence refactorings G Varoquaux 10
  16. 16. 1 How I work progressive consolidation Start with a script playing to understand the problem Identify blocks/operations ⇒ move to a function As they stabilize, move to a module Clean: delete code & files you have version control Attentional load makes it impossible to find or understand things Where’s Waldo?G Varoquaux 10
  17. 17. 1 How I work progressive consolidation Start with a script playing to understand the problem Identify blocks/operations ⇒ move to a function As they stabilize, move to a module Clean: delete code & files you have version control Why is it hard? Long compute times make us unadventurous Know your tools Refactoring editor Version control G Varoquaux 10
  18. 18. 1 Mitigating long compute times by storing intermediate results Makefiles Accessible intermediate results Break control flow & error management G Varoquaux 11
  19. 19. 1 Mitigating long compute times by storing intermediate results Makefiles Accessible intermediate results Break control flow & error management Memoization / caching g = mem.cache(f) # ”joblib” Python module b = g(a) # computes a using f c = g(a) # retrieves results from store Fits in coding / experimentation loop Black-boxy, persistence only implicit Discourages refactoring (avoid recomputing) G Varoquaux 11
  20. 20. 1 A design pattern in computational experiments MVC pattern (from Wikipedia Model Manages the data and rules of the application View Output represen- tation Possibly several views Controller Accepts input and converts it to commands for model and view Photo-editing software Filters Canvas Tool palettes Typical web application Database Web-pages URLs G Varoquaux 12
  21. 21. 1 A design pattern in computational experiments MVC pattern (from Wikipedia Model Manages the data and rules of the application View Output represen- tation Possibly several views Controller Accepts input and converts it to commands for model and view For data science: Numerical, data- processing, & ex- perimental logic Results, as files. Data & plots Imperative API Avoid input as files: not expressive Module with functions Post-processing script CSV & data files Script ⇒ for loops G Varoquaux 12
  22. 22. 1 A design pattern in computational experiments MVC pattern (from Wikipedia Model Manages the data and rules of the application View Output represen- tation Possibly several views Controller Accepts input and converts it to commands for model and view For data science: Numerical, data- processing, & ex- perimental logic Results, as files. Data & plots Imperative API Avoid input as files: not expressive Module with functions Post-processing script CSV & data files Script ⇒ for loops A recipe 3 types of files: • modules • command scripts • post-processing scripts CSVs & intermediate data files Separate computation from analysis / plotting Code and text (and data) ⇒ version control G Varoquaux 12
  23. 23. 1 A design pattern in computational experiments MVC pattern (from Wikipedia Model Manages the data and rules of the application View Output represen- tation Possibly several views Controller Accepts input and converts it to commands for model and view For data science: Numerical, data- processing, & ex- perimental logic Results, as files. Data & plots Imperative API Avoid input as files: not expressive Module with functions Post-processing script CSV & data files Script ⇒ for loops A recipe 3 types of files: • modules • command scripts • post-processing scripts CSVs & intermediate data files Separate computation from analysis / plotting Code and text (and data) ⇒ version control Decouple steps Goals: Reuse code Mitigate compute time G Varoquaux 12
  24. 24. Adopting software-engineering best practices G Varoquaux 13
  25. 25. 1 The ladder of code quality Use static checking in your editor seriously G Varoquaux 14
  26. 26. 1 The ladder of code quality Use static checking in your editor seriously Coding convention, good naming “Any fool can write code that a computer can understand. Good programmers write code that humans can understand” – Martin Fowler Your future self will thank you G Varoquaux 14
  27. 27. 1 The ladder of code quality Use static checking in your editor seriously Coding convention, good naming Version control Use git + github Code review Unit testing If it’s not tested, it’s broken or soon will be Make a package controlled dependencies and compilation ... G Varoquaux 14
  28. 28. 1 The ladder of code qualityIncreasingcost ? Use static checking in your editor seriously Coding convention, good naming Version control Use git + github Code review Unit testing If it’s not tested, it’s broken or soon will be Make a package controlled dependencies and compilation ... Premature software engineering is the root of reproducible science evil G Varoquaux 14
  29. 29. 1 The ladder of code qualityIncreasingcost ? Use static checking in your editor seriously Coding convention, good naming Version control Use git + github Code review Unit testing If it’s not tested, it’s broken or soon will be Make a package controlled dependencies and compilation ... Premature software engineering is the root of reproducible science evil Over versus under engineering Our goal is scientific discovery Experimentation to develop intuitions ⇒ new ideas As the path becomes clear: consolidation Heavy engineering too early freezes bad ideas G Varoquaux 14
  30. 30. 1 LibrariesIncreasingcost ? Use static checking in your editor seriously Coding convention, good naming Version control Use git + github Code review Unit testing If it’s not tested, it’s broken or soon will be Make a package controlled dependencies and compilation ... A library G Varoquaux 15
  31. 31. 1 LibrariesIncreasingcost ? Use static checking in your editor seriously Coding convention, good naming Version control Use git + github Code review Unit testing If it’s not tested, it’s broken or soon will be Make a package controlled dependencies and compilation ... A library Code = Software Well-defined problem = research Functions should work as long as input is valid Targets users, not the authors Made for reuse The workhorse of reproducibility G Varoquaux 15
  32. 32. 1 Libraries enable reproducible science Reproducibility Rerun and come to the same conclusion An argument for copying all scripts for each paper Better: a tag in git G Varoquaux 16
  33. 33. 1 Libraries enable reproducible science Reproducibility Rerun and come to the same conclusion An argument for copying all scripts for each paper Better: a tag in git Copying scripts scales very poorly: Accumulation of half-dead code ⇒ cognitive overload No consolidation across studies Each study has different variants of different bugs Garden of forking code G Varoquaux 16
  34. 34. 1 Libraries enable reproducible science Reproducibility Rerun and come to the same conclusion An argument for copying all scripts for each paper Better: a tag in git Kirstie Whitaker G Varoquaux 16
  35. 35. 1 Libraries enable reproducible science Reproducibility Rerun and come to the same conclusion An argument for copying all scripts for each paper Frozen food Reusability Apply the approach to a new problem Being able to understand, modify, run in new settings Generalization is the true scientific test Reusable computational science = libraries G Varoquaux 16
  36. 36. 2 Making libraries to enable reproducibility G Varoquaux 17
  37. 37. 2 Pick the right battles: project scope Project idea A software implementing: i) machine learning and ii) neuroimaging and iii) a graphical user interface and iv) 3D plotting G Varoquaux 18
  38. 38. 2 Pick the right battles: project scope Project idea A software implementing: i) machine learning and ii) neuroimaging and iii) a graphical user interface and iv) 3D plotting G Varoquaux 18
  39. 39. 2 Pick the right battles: project scope Project idea A software implementing: i) machine learning and ii) neuroimaging and iii) a graphical user interface and iv) 3D plotting Define project scope and vision Break down projects by expertise Don’t solve hard problems Know the software landscape Need a vision = elevator pitch G Varoquaux 18
  40. 40. 2 Pick the right battles: project scope Project idea A software implementing: i) machine learning and ii) neuroimaging and iii) a graphical user interface and iv) 3D plotting Define project scope and vision Break down projects by expertise Don’t solve hard problems Know the software landscape Need a vision = elevator pitch Your research (PhD) probably does not qualify ⇒ need to cherry-pick contributions G Varoquaux 18
  41. 41. 2 Short cycles, limited ambitions Keep coming back to your users Release early, release often G Varoquaux 19
  42. 42. 2 Simplicity Complexity increase superlinearly [An Experiment on Unit Increase in Problem Complexity, Woodfield 1979] 25% increase in problem complexity ⇒ 100% increase in code complexity G Varoquaux 20
  43. 43. 2 Simplicity Complexity increase superlinearly [An Experiment on Unit Increase in Problem Complexity, Woodfield 1979] 25% increase in problem complexity ⇒ 100% increase in code complexity The 80/20 rule 80% of the usecases can be solved with 20% of the lines of code Avoid feature creep G Varoquaux 20
  44. 44. 2 Quality Quality is free This is a book, by Philip Crosby G Varoquaux 21
  45. 45. 2 You need quality Quality will give you users Bugs give you bad rap Quality will give you developers Contribute to learn and improve Quality avoids scientific errors Do less, do better Goes against the grant-system incentive G Varoquaux 22
  46. 46. 2 Quality: what & how Great documentation Simplify, but don’t dumb down Focus on what the user is trying to solve Great APIs Example-based development If something is hard to explain, rethink the concepts Limit the number of different concepts and objects Good numerics Write tests based on mathematical properties When a user finds an instability, write a new test G Varoquaux 23
  47. 47. 2 Quality: what & how Great documentation Simplify, but don’t dumb down Focus on what the user is trying to solve Great APIs Example-based development If something is hard to explain, rethink the concepts Limit the number of different concepts and objects Good numerics Write tests based on mathematical properties When a user finds an instability, write a new test Quality enables reuse Beyond mere reproducibility G Varoquaux 23
  48. 48. 2 Quality assurance Code review: pull requests Can include newcomers We read each others code Everything is discussed: - Should the algorithm go in? - Are there good defaults? - Are names meaningfull? - Are the numerics stable? - Could it be faster? G Varoquaux 24
  49. 49. 2 Quality assurance Unit testing Everything is tested Great for numerics Overall tests enforce on all estimators - consistency with the API - basic invariances - good handling of various inputs G Varoquaux 25
  50. 50. 2 Dependencies Beyond installation, a challenge is to ensure package versions play way together: correctness of the code Breakage of backward compability yields irreconcilable dependencies G Varoquaux 26
  51. 51. 2 Dependencies and their upgrade It’s a fact: users hate upgrading If it ain’t broken, don’t fix it even if it is, apparently G Varoquaux 27
  52. 52. 2 Declaring undependence? Monolythic packages with no dependencies... But: Scaling is hard Complexity grows as square of codebase size [Woodfield 1979] User support grows with userbase size G Varoquaux 28
  53. 53. 3 From software back to science G Varoquaux 29
  54. 54. 3 Beyond computational reproducibility Make every computational step reproducible, and good science will emerge G Varoquaux 30
  55. 55. 3 Beyond computational reproducibility Make every computational step reproducible, and good science will emerge Estimating the reproducibility of psychological science [Science 2015] 36% of effects replicate Reasons: Statistical challenges — analysis degrees of freedom Weak insentives — winner’s curse in publication Seldom computational reproducibility G Varoquaux 30
  56. 56. In practice, the best way to improve research is to use the right (conceptual) tools. G Varoquaux 31
  57. 57. 3 Managing complexity In practice, the best way to improve research is to use the right (conceptual) tools. The everyday roadblock is cognitive load Machine learning, brain anatomy, psychology R, Python, shell scripts Funding agencies, reviewer 3, the Manuscript G Varoquaux 32
  58. 58. 3 Tools that reduce cognitive overload It’s a design problem G Varoquaux 33
  59. 59. 3 Tools that reduce cognitive overload Jonathan Ive, an industrial designer, is #4 at Apple Code different. G Varoquaux 34
  60. 60. 3 Some API design principles for the scipy stack Consistency, consistency, consistency Functions are easier to understand than classes A library should hinge on a small number of concepts Common data containers make the ecosystem stronger Each function should have one and only one purpose Code for interfaces, but don’t overdo duck typing Properties are for impedance matching Shallow is better than deep Error messages matter Be Pythonic G Varoquaux 35
  61. 61. 3 Some API design principles for the scipy stack Consistency, consistency, consistency np.save(file, obj) pickle.dump(obj, file) fmin(...maxiter=10) lsq linear(...max iter=10) Creates cognitive overload Functions are easier to understand than classes A library should hinge on a small number of concepts Common data containers make the ecosystem stronger Each function should have one and only one purpose Code for interfaces, but don’t overdo duck typing Properties are for impedance matching Shallow is better than deep Error messages matter Be Pythonic G Varoquaux 36
  62. 62. 3 Some API design principles for the scipy stack Consistency, consistency, consistency Functions are easier to understand than classes Objects have hidden states, Objects have no universal interface, entry point, output A library should hinge on a small number of concepts Common data containers make the ecosystem stronger Each function should have one and only one purpose Code for interfaces, but don’t overdo duck typing Properties are for impedance matching Shallow is better than deep Error messages matter Be Pythonic G Varoquaux 37
  63. 63. 3 Some API design principles for the scipy stack Consistency, consistency, consistency Functions are easier to understand than classes A library should hinge on a small number of concepts How much do usage patterns carry out across the library? Common data containers make the ecosystem stronger Each function should have one and only one purpose Code for interfaces, but don’t overdo duck typing Properties are for impedance matching Shallow is better than deep Error messages matter Be Pythonic G Varoquaux 38
  64. 64. 3 Some API design principles for the scipy stack Consistency, consistency, consistency Functions are easier to understand than classes A library should hinge on a small number of concepts Common data containers make the ecosystem stronger Facilitates working with multiple libraries together Easier to get up to speed with a given library Each function should have one and only one purpose Code for interfaces, but don’t overdo duck typing Properties are for impedance matching Shallow is better than deep Error messages matter Be Pythonic G Varoquaux 39
  65. 65. 3 Some API design principles for the scipy stack Consistency, consistency, consistency Functions are easier to understand than classes A library should hinge on a small number of concepts Common data containers make the ecosystem stronger Each function should have one and only one purpose Change of behavior depending on input type Code for interfaces, but don’t overdo duck typing Properties are for impedance matching Shallow is better than deep Error messages matter Be Pythonic G Varoquaux 40
  66. 66. 3 Some API design principles for the scipy stack Consistency, consistency, consistency Functions are easier to understand than classes A library should hinge on a small number of concepts Common data containers make the ecosystem stronger Each function should have one and only one purpose Code for interfaces, but don’t overdo duck typing Interfaces define objects Incompatible behaviors lead to bugs (eg np.matrix) Properties are for impedance matching Shallow is better than deep Error messages matter Be Pythonic G Varoquaux 41
  67. 67. 3 Some API design principles for the scipy stack Consistency, consistency, consistency Functions are easier to understand than classes A library should hinge on a small number of concepts Common data containers make the ecosystem stronger Each function should have one and only one purpose Code for interfaces, but don’t overdo duck typing Properties are for impedance matching Properties obfuscate the data model of the object Properties can create hidden compute costs Shallow is better than deep Error messages matter Be Pythonic G Varoquaux 42
  68. 68. 3 Some API design principles for the scipy stack Consistency, consistency, consistency Functions are easier to understand than classes A library should hinge on a small number of concepts Common data containers make the ecosystem stronger Each function should have one and only one purpose Code for interfaces, but don’t overdo duck typing Properties are for impedance matching Shallow is better than deep Objects are understood by their surface Composition creates cognitive overload Error messages matter Be Pythonic G Varoquaux 43
  69. 69. 3 Some API design principles for the scipy stack Consistency, consistency, consistency Functions are easier to understand than classes A library should hinge on a small number of concepts Common data containers make the ecosystem stronger Each function should have one and only one purpose Code for interfaces, but don’t overdo duck typing Properties are for impedance matching Shallow is better than deep Error messages matter Explain the problem Print the offending value Be Pythonic G Varoquaux 44
  70. 70. 3 Some API design principles for the scipy stack Consistency, consistency, consistency Functions are easier to understand than classes A library should hinge on a small number of concepts Common data containers make the ecosystem stronger Each function should have one and only one purpose Code for interfaces, but don’t overdo duck typing Properties are for impedance matching Shallow is better than deep Error messages matter Be Pythonic Avoid syntax hacks G Varoquaux 45
  71. 71. 3 Scikit-learn API Scikit-learn cheat sheet Scikit-learn Fit and predict >>> estimator = Estimator(param1=param1) >>> estimator.fit(X train, y train) >>> y test = estimator.predict(X test) Transform data >>> X red = estimator.transform(X test) G Varoquaux 46
  72. 72. 3 Scikit-learn API Scikit-learn cheat sheet Scikit-learn Fit and predict >>> estimator = Estimator(param1=param1) >>> estimator.fit(X train, y train) >>> y test = estimator.predict(X test) Transform data >>> X red = estimator.transform(X test) The estimator is a “contract” (slightly more elaborate than above) It has created an ecosystem of packages Based on duck-typing, not inheritence G Varoquaux 46
  73. 73. 3 Example-driven development The 3-liner as the new cool Teaching others Teaching yourself Write examples that solve end problems Iterate on your API until these are simple G Varoquaux 47
  74. 74. 3 Example-driven development The 3-liner as the new cool Teaching others Teaching yourself Write examples that solve end problems Iterate on your API until these are simple User flow on the scikit-learn website: Examples G Varoquaux 47
  75. 75. 3 Example-driven development The 3-liner as the new cool Teaching others Teaching yourself Write examples that solve end problems Iterate on your API until these are simple User flow on the nilearn website: Examples G Varoquaux 47
  76. 76. 3 Building great documentation Focus on explaining concepts (hint: write plain English) Less is more: prioritize, avoid redundancy Code examples must be short (link to full tutorial examples) Links everywhere: users will land at the wrong place Teach with the docs Plan for maintenance of docs: Continuous integration Check links Runs examples Doctests G Varoquaux 48
  77. 77. 3 Reusable science scikit-learn is the new machine-learning textbook nilearn is the new neuroimaging review article Experiments reproduced at each commit eg: brain reading nilearn.github.io/auto examples/02 decoding/plot miyawaki reconstruction.html G Varoquaux 49
  78. 78. 3 Reusable science scikit-learn is the new machine-learning textbook nilearn is the new neuroimaging review article Experiments reproduced at each commit eg: brain reading nilearn.github.io/auto examples/02 decoding/plot miyawaki reconstruction.html Resource intensive Continuous integration: Data ⇒ Fight for good open data Computation ⇒ Find good algorithms and tradeoffs Forces us to distill the literature (as a review) G Varoquaux 49
  79. 79. 3 Reusable science scikit-learn is the new machine-learning textbook nilearn is the new neuroimaging review article Experiments reproduced at each commit eg: brain reading nilearn.github.io/auto examples/02 decoding/plot miyawaki reconstruction.html Package development consolidates science and moves it outside the lab G Varoquaux 49
  80. 80. 3 Reusable science scikit-learn is the new machine-learning textbook nilearn is the new neuroimaging review article Experiments reproduced at each commit eg: brain reading nilearn.github.io/auto examples/02 decoding/plot miyawaki reconstruction.html Package development consolidates science and moves it outside the lab scipy-lectures: living book for Python in science G Varoquaux 49
  81. 81. Coding as a scientist Final code should be auditable, ideally reusable Tension between interactive computing & automating Main enemy: cognitive overload G Varoquaux 50
  82. 82. Coding as a scientist Final code should be auditable, ideally reusable Tension between interactive computing & automating Main enemy: cognitive overload In the industry Reusable Verifiable? Not for silicon valley, but in insurance, healthcare, banking... Moving data-scientist code to production? Software projects going over budget? G Varoquaux 50
  83. 83. @GaelVaroquaux Computational practices for reproducible science Software-engineering skills are superpowers “text-book” software engineering (yet never taught) Make you more productive
  84. 84. @GaelVaroquaux Computational practices for reproducible science Software-engineering skills are superpowers “text-book” software engineering (yet never taught) Make you more productive But beware of over-engineering In software development, keep an eye on the end goal Science is iterative, not every step must be reproduced
  85. 85. @GaelVaroquaux Computational practices for reproducible science Software-engineering skills are superpowers “text-book” software engineering (yet never taught) Make you more productive But beware of over-engineering In software development, keep an eye on the end goal Science is iterative, not every step must be reproduced Science is made by humans, not computers Usability, communication, human factors Design for reuse & generalization
  86. 86. @GaelVaroquaux Computational practices for reproducible science Software-engineering skills are superpowers “text-book” software engineering (yet never taught) Make you more productive But beware of over-engineering In software development, keep an eye on the end goal Science is iterative, not every step must be reproduced Science is made by humans, not computers Usability, communication, human factors Design for reuse & generalization Reproducible research must borrow from agile development

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