![Python example
# Setup indicators
ix_gdp = 7
...
# Load & clean data
m_raw = np.recfromcsv('bea_gdp.csv')
gdp = m_raw[:, ix_gdp]
...
Benjamin S. Skrainka Tune up your data science process February 10, 2016 22 / 24](https://image.slidesharecdn.com/tuneup-160212022820/85/Tune-up-your-data-science-process-22-320.jpg)
Tune up your data science process
- 1. Tune up your data science process Benjamin S. Skrainka February 10, 2016 Benjamin S. Skrainka Tune up your data science process February 10, 2016 1 / 24
- 2. The correctness problem A lot of (data) science is unscientific: “My code runs, so the answer must be correct” “It passed Explain Plan, so the answer is correct” “This model is too complex to have a design document” “It is impossible to unit test scientific code” “The lift from the direct mail campaign is 10%” Benjamin S. Skrainka Tune up your data science process February 10, 2016 2 / 24
- 3. Correctness matters Bad (data) science: Costs real money and can kill people Will eventually damage your reputation and career Could expose you to litigation An issue of basic integrity and sleeping at night Benjamin S. Skrainka Tune up your data science process February 10, 2016 3 / 24
- 4. Objectives Today’s goals: Introduce VV&UQ framework to evaluate correctness of scientific models Survey good habits to improve quality of your work Benjamin S. Skrainka Tune up your data science process February 10, 2016 4 / 24
- 5. Verification, Validation, & Uncertainty Quantification Benjamin S. Skrainka Tune up your data science process February 10, 2016 5 / 24
- 6. Introduction to VV&UQ Verification, Validation, & Uncertainty Quantification provides epistemological framework to evaluate correctness of scientific models: Evidence of correctness should accompany any prediction In absence of evidence, assume predictions are wrong Popper: can only disprove or fail to disprove a model VV&UQ is inductive whereas science is deductive Reference: Verification and Validation in Scientific Computing by Oberkampf & Roy Benjamin S. Skrainka Tune up your data science process February 10, 2016 6 / 24
- 7. Definitions of VV&UQ Definitions of terms (Oberkampf & Roy): Verification: “solving equations right” I.e., code implements the model correctly Validation: “solving right equations” I.e., model has high fidelity to reality Definitions of VV&UQ will vary depending on source . . . → Most organizations do not even practice verification. . . Benjamin S. Skrainka Tune up your data science process February 10, 2016 7 / 24
- 8. Definition of UQ Definition of Uncertainty Quantification (Oberkampf & Roy): Process of identifying, characterizing, and quantifying those factors in an analysis which could affect accuracy of computational results Do your assumptions hold? When do they fail? Does your model apply to the data/situation? Where does your model break down? What are its limits? Benjamin S. Skrainka Tune up your data science process February 10, 2016 8 / 24
- 9. Verification of code Does your code implement the model correctly? Unit test everything you can: Scientific code can be unit tested Test special cases Test on cases with analytic solutions Test on synthetic data Unit test framework will setup and tear-down fixtures Should be able to recover parameters from Monte Carlo data Benjamin S. Skrainka Tune up your data science process February 10, 2016 9 / 24
- 10. Verification of SQL Passing Explain Plan doesn’t mean your SQL is correct: Garbage in, garbage out Check a simple case you can compute by hand Check join plan is correct Check aggregate statistics Check answer is compatible with reality Benjamin S. Skrainka Tune up your data science process February 10, 2016 10 / 24
- 11. Unit test import unittest2 as unittest import assignment as problems class TestAssignment(unittest.TestCase): def test_zero(self): result = problems.question_zero() self.assertEqual(result, 9198) ... if __name__ == '__main__': unittest.main() Benjamin S. Skrainka Tune up your data science process February 10, 2016 11 / 24
- 12. Unit test Figure 1:Benjamin S. Skrainka Tune up your data science process February 10, 2016 12 / 24
- 13. Validation of model Check your model is a good (enough) representation of reality: “All models are wrong but some are useful” – George Box Run an experiment Perform specification testing Test assumptions hold Beware of endogenous features Benjamin S. Skrainka Tune up your data science process February 10, 2016 13 / 24
- 14. Approaches to experimentation Many ways to test: A/B test Multi-armed bandit Bayesian A/B test Wald sequential analysis Benjamin S. Skrainka Tune up your data science process February 10, 2016 14 / 24
- 15. Uncertainty quantification There are many types of uncertainty which affect the robustness of your model: Parameter uncertainty Structural uncertainty Algorithmic uncertainty Experimental uncertainty Interpolation uncertainty Classified as aleatoric (statistical) and epistemic (systematic) Benjamin S. Skrainka Tune up your data science process February 10, 2016 15 / 24
- 16. Good habits Benjamin S. Skrainka Tune up your data science process February 10, 2016 16 / 24
- 17. Act like a software engineer Use best practices from software engineering: Good design of code Follow a sensible coding convention Version control Use same file structure for every project Unit test Use PEP8 or equivalent Perform code reviews Benjamin S. Skrainka Tune up your data science process February 10, 2016 17 / 24
- 18. Reproducible research ‘Document what you do and do what you document’: Keep a journal! Data provenance How data was cleaned Design document Specification & requirements Do you keep a journal? You should. Fermi taught me that. – John A. Wheeler Benjamin S. Skrainka Tune up your data science process February 10, 2016 18 / 24
- 19. Follow a workflow Use a workflow like CRISP-DM: 1 Define business question and metric 2 Understand data 3 Prepare data 4 Build model 5 Evaluate 6 Deploy Ensures you don’t forget any key steps Benjamin S. Skrainka Tune up your data science process February 10, 2016 19 / 24
- 20. Automate your data pipeline One-touch build of your application or paper: Automate entire workflow from raw data to final result Ensures you perform all steps Ensures all steps are known – no one off manual adjustments Avoids stupid human errors Auto generate all tables and figures Save time when handling new data . . . which always has subtle changes in formatting Benjamin S. Skrainka Tune up your data science process February 10, 2016 20 / 24
- 21. Write flexible code to handle data Use constants/macros to access data fields: Code will clearly show what data matters Easier to understand code and data pipeline Easier to debug data problems Easier to handles changes in data formatting Benjamin S. Skrainka Tune up your data science process February 10, 2016 21 / 24
- 22. Python example # Setup indicators ix_gdp = 7 ... # Load & clean data m_raw = np.recfromcsv('bea_gdp.csv') gdp = m_raw[:, ix_gdp] ... Benjamin S. Skrainka Tune up your data science process February 10, 2016 22 / 24
- 23. Politics. . . Often, there is political pressure to violate best practice: Examples: 80% confidence intervals Absurd attribution window Two year forecast horizon but only three months of data Hard to do right thing vs. senior management Recruit a high-level scientist to advocate Particularly common with forecasting: Often requested by management for CYA Insist on a ‘panel of experts’ for impossible decisions Benjamin S. Skrainka Tune up your data science process February 10, 2016 23 / 24
- 24. Conclusion Need to raise the quality of data science: VV & UQ provides rigorous framework: Verification: solve the equations right Validation: solve the right equations Uncertainty quantification: how robust is model to unknowns? Adopting good habits provides huge gains for minimal effort Benjamin S. Skrainka Tune up your data science process February 10, 2016 24 / 24