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Moving from Artisanal to Industrial Machine Learning
1.
© 2019 KNIME
AG. All Rights Reserved. Moving from Artisanal to Industrial Machine Learning Greg Landrum (greg.landrum@knime.com)
2.
© 2019 KNIME
AG. All Rights Reserved. 2 This talk • Motivation • Creating a reproducible/industrial artisan • An artisanal side trip into working with imbalanced data
3.
© 2019 KNIME
AG. All Rights Reserved. 3 Context Artisanal Industrial https://flic.kr/p/RJ5xEs License: CC-BY 2.0CC BY 2.0, https://flic.kr/p/a3LLdm
4.
© 2019 KNIME
AG. All Rights Reserved. 4 Context Artisanal • Creative/Exploratory • Flexible Industrial • Automated • Reproducible • Repeatable • Quality control
5.
© 2019 KNIME
AG. All Rights Reserved. 5 Motivation: utility • Thinking about the models that are useful in the design-make-test cycle of a med-chem project • Perhaps something project-specific for the main target + important anti-targets. • Likely a host of additional global models that could be used (solubility, pKa, hERG, CYPs, synthetic accessibility, etc.)
6.
© 2019 KNIME
AG. All Rights Reserved. 6 Aspirations • Can we figure out how to help the artisan be more reproducible/repeatable? • Can we provide an “industrial” framework the artisan can work within? • Can this somehow be practical?
7.
7© 2019 KNIME
AG. All Rights Reserved. A process for data mining
8.
© 2019 KNIME
AG. All Rights Reserved. 8 Cross-industry standard process for data mining • An EU-funded project from the late ‘90s run by Integral Solutions (bought by SPSS, bought by IBM), Teradata, Daimler-Benz, NCR, and OHRA.
9.
© 2019 KNIME
AG. All Rights Reserved. 9 Cross-industry standard process for data mining • An EU-funded project from the late ‘90s run by Integral Solutions (bought by SPSS, bought by IBM), Teradata, Daimler-Benz, NCR, and OHRA. I can guess what you’re thinking…
10.
© 2019 KNIME
AG. All Rights Reserved. 10 Cross-industry standard process for data mining • An EU-funded project from the late ‘90s run by Integral Solutions (bought by SPSS, bought by IBM), Teradata, Daimler-Benz, NCR, and OHRA. I can guess what you’re thinking…
11.
© 2019 KNIME
AG. All Rights Reserved. 11 Cross-industry standard process for data mining • An EU-funded project from the late ‘90s run by Integral Solutions (bought by SPSS, bought by IBM), Teradata, Daimler-Benz, NCR, and OHRA. Shockingly, this actually produced something useful
12.
© 2019 KNIME
AG. All Rights Reserved. 12 The CRISP-DM Process 12 CRISP-DM (CRoss Industry Standard Process for Data Mining) is a standard process for data mining solutions. Image from: https://upload.wikimedia.org/wikipedia/commons /b/b9/CRISP-DM_Process_Diagram.png
13.
© 2019 KNIME
AG. All Rights Reserved. 13 Establishing context • Business understanding – What problem are we trying to solve? – What would a solution look like? • Data understanding – What data do we have available? – Is it any good? – What might be useful for this problem? Image from: https://upload.wikimedia.org/wikipedia/commons/b/b9/CRISP- DM_Process_Diagram.png Domain expertise required here
14.
© 2019 KNIME
AG. All Rights Reserved. 14 The problem • Build predictive models for bioactivity based on the data in screening assays
15.
© 2019 KNIME
AG. All Rights Reserved. 15 The datasets we’ll be working with • qHTS data from eight PubChem assays captured in ChEMBL • The assays have very different numbers of actives in them, so to get started we’ll use two at different ends of the spectrum
16.
© 2019 KNIME
AG. All Rights Reserved. 16 The datasets we’ll be working with • Assay CHEMBL1614166 (PubChem BioAssay. qHTS Assay for Inhibitors of MBNL1-poly(CUG) RNA binding. (Class of assay: confirmatory)) – https://www.ebi.ac.uk/chembl/assay_report_card/CHEMBL1614166/ – https://pubchem.ncbi.nlm.nih.gov/bioassay/2675 • 34018 inactives, 98 actives (using the annotations from PubChem)
17.
© 2019 KNIME
AG. All Rights Reserved. 17 Nature of the actives (CHEMBL1614166)
18.
© 2019 KNIME
AG. All Rights Reserved. 18 Nature of the actives (CHEMBL1614166)
19.
© 2019 KNIME
AG. All Rights Reserved. 19 The datasets we’ll be working with • Assay CHEMBL1614421 (PUBCHEM_BIOASSAY: qHTS for Inhibitors of Tau Fibril Formation, Thioflavin T Binding. (Class of assay: confirmatory)) – https://www.ebi.ac.uk/chembl/assay_report_card/CHEM BL1614166/ – https://pubchem.ncbi.nlm.nih.gov/bioassay/1460 • 43345 inactives, 5602 actives (using the annotations from PubChem)
20.
© 2019 KNIME
AG. All Rights Reserved. 20 Model building • Data Preparation – Making it machine-useable – Cleanup – Feature engineering • Modeling – The cool ML/AI stuff Image from: https://upload.wikimedia.org/wikipedia/commons/b/b9/CRISP- DM_Process_Diagram.png
21.
© 2019 KNIME
AG. All Rights Reserved. 21 Data Preparation • Structures are taken from ChEMBL – Already some standardization done – Processed with RDKit • Fingerprints: RDKit Morgan-2, 2048 bits
22.
© 2019 KNIME
AG. All Rights Reserved. 22 Modeling • Stratified 80-20 training/holdout split • KNIME random forest classifier – 500 trees – Max depth 15 – Min node size 2 This is a first pass through the cycle, we will try other fingerprints, learning algorithms, and hyperparameters in future iterations
23.
© 2019 KNIME
AG. All Rights Reserved. 23 Evaluation • Does the model work? • Does it actually solve the problem? • Was the problem well posed? • Is it implying data problems? Image from: https://upload.wikimedia.org/wikipedia/commons/b/b9/CRISP- DM_Process_Diagram.png
24.
© 2019 KNIME
AG. All Rights Reserved. 24 Evaluation • AUROC, overall accuracy and Cohen’s kappa on the holdout data Many, many, many options here. I’m using global metrics because in the end I want to use the “active/inactive” predictions made by the model
25.
© 2019 KNIME
AG. All Rights Reserved. 25 Using • Deployment – How do you actually use the model? – How do you keep it up to date? – How do you get people to accept the results? Image from: https://upload.wikimedia.org/wikipedia/commons/b/b9/CRISP- DM_Process_Diagram.png
26.
© 2019 KNIME
AG. All Rights Reserved. 26 Deployment: technical • Easy since I’m using KNIME • Deploy as a web service – Easy to validate/test • Automated rebuild/re-evaluate when new data are available
27.
© 2019 KNIME
AG. All Rights Reserved. 27 Deployment: practical • Providing “active/inactive” classifications and predicted probabilities likely not enough • Similar compounds from training set? • Applicability domain? • Conformal prediction? • “Explanation” of the prediction (i.e. similarity maps)?
28.
28© 2019 KNIME
AG. All Rights Reserved. Results
29.
© 2019 KNIME
AG. All Rights Reserved. 29 Evaluation CHEMBL1614166: holdout data
30.
© 2019 KNIME
AG. All Rights Reserved. 30 Evaluation CHEMBL1614166: test data AUROC=0.72
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AG. All Rights Reserved. 31 Results CHEMBL1614421: holdout data
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AG. All Rights Reserved. 32 Evaluation CHEMBL1614421: holdout data AUROC=0.75
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AG. All Rights Reserved. 33 Taking stock • Both models have: – Good overall accuracies (because of imbalance) – Decent AUROC values – Terrible Cohen kappas Now what?
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34© 2019 KNIME
AG. All Rights Reserved. Let’s get artisanal…
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AG. All Rights Reserved. 35 Quick diversion on bag classifiers When making predictions, each tree in the classifier votes on the result. Majority wins The predicted class probabilities are often the means of the predicted probabilities from the individual trees We construct the ROC curve by sorting the predictions in decreasing order of predicted probability of being active. Note that the actual predictions are irrelevant for an ROC curve. As long as true actives tend to have a higher predicted probability of being active than true inactives the AUC will be good.
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AG. All Rights Reserved. 36 Handling imbalanced data • The standard decision rule for a random forest (or any bag classifier) is that the majority wins1, i.e. at the predicted probability of being active must be >=0.5 in order for the model to predict "active" • Shift that threshold to a lower value for models built on highly imbalanced datasets2 1 This is only strictly true for binary classifiers 2 Chen, J. J., et al. “Decision Threshold Adjustment in Class Prediction.” SAR and QSAR in Environmental Research 17 (2006): 337–52.
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AG. All Rights Reserved. 37 Picking a new decision threshold • Generate a random forest for the dataset using the training set • Generate out-of-bag predicted probabilities using the training set • Try a number of different decision thresholds1 and pick the one that gives the best kappa • Once we have the decision threshold, use it to generate predictions for the test set. 1 Here we use: [0.05, 0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 ]
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AG. All Rights Reserved. 38 Results CHEMBL1614166 • Balanced confusion matrix Previously 0.181
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AG. All Rights Reserved. 39 • Balanced confusion matrix Results CHEMBL1614421 Previously 0.005
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AG. All Rights Reserved. 40 Does it work in general? ChEMBL data, random-split validation
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AG. All Rights Reserved. 41 Does it work in general? Proprietary data, time-split validation
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AG. All Rights Reserved. 42 Coming back to validation • CHEMBL1614166: – Overall accuracy: 99.8% – Kappa: 0.53 – AUROC: 0.72 • CHEMBL1614421: – Overall accuracy: 89.6% – Kappa: 0. 30 – AUROC: 0.75
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AG. All Rights Reserved. 43 Wrapping up Image from: https://upload.wikimedia.org/wikipedia/commons /b/b9/CRISP-DM_Process_Diagram.png
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AG. All Rights Reserved. 44 Maybe useful… • “Practical Machine Learning Canvas”
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AG. All Rights Reserved. 45 Data/Scripts • KNIME workflow for adjusting the decision threshold: https://kni.me/w/HRDmzyQy0UL0k7H2 • RDKit blog post about adjusting the decision threshold (includes links to code): http://rdkit.blogspot.com/2018/11/working-with- unbalanced-data-part-i.html • Practical ML Canvas: https://bit.ly/2JLLsRC
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AG. All Rights Reserved. 46 Acknowledgements • Dean Abbott (Abbott Analytics) • KNIME: – Daria Goldmann – Rosaria Silipo • NIBR: – Nik Stiefl – Nadine Schneider – Niko Fechner For more amazing car pictures: do an image search for “rat rod”
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