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QA Fest 2019. Никита Кричко. Тестирование приложений, использующих ИИ
- 1. Тема доклада Тема доклада Тема доклада KYIV 2019 Кричко Никита ML application testing QA CONFERENCE #1 IN UKRAINE
- 2. About me Krychko Mykyta Performance test architect Like: - Puzzle - ML - Data science - Data science puzzle Nik.krichko@gmail.com
- 3. Our hero Onufrious - Millennial - Tank driver - traveller - Cannot live without smartphone - Game of thrones fan
- 4. Data science errors The Neural Net Tank Urban Legend https://www.gwern.net/Tanks
- 5. Data science errors The Neural Net Tank Urban Legend https://www.gwern.net/Tanks
- 6. Data science errors The Neural Net Tank Urban Legend https://www.gwern.net/Tanks
- 7. Data science errors UBER - supply and demand
- 8. Data science errors Recommendation system from one IP
- 9. Data science errors WHY I see ads about pregnancy termination?
- 10. Errors • Critical • Fraud • Unethical
- 11. Errors: based on type of ML Supervised Unsupervised
- 12. ML TASKS SUPERVISED: • Classification • Regression • Forecast UNSUPERVISED: • Clustering • Outlier detection • Dimensionality reduction
- 16. UNSUPERVISED
- 19. CLASSIFICATION Define object class Hotdog not hotdog
- 20. CLASSIFICATION TRAIN data set Main hormone Long hair has_hotdog Sex testosterone 0 1 male estrogen 1 0 female testosterone 0 1 male estrogen 1 0 female testosterone 1 1 male testosterone 0 1 male testosterone 0 0 male testosterone 0 1 male testosterone 1 1 male testosterone 0 1 male
- 21. CLASSIFICATION TRAIN data set Main hormone Long hair has_hotdog Sex testosterone 0 1 male estrogen 1 0 female testosterone 0 1 male estrogen 1 0 female testosterone 1 1 male testosterone 0 1 male testosterone 0 0 male testosterone 0 1 male testosterone 1 1 male testosterone 0 1 male Imbalanced data 20% female 80% male
- 22. Imbalanced data Over-sampling minority class under-sampling majority class Both library(ROSE) undersampling_result <- ovun.sample(Class ~ ., data = Dataset, method = {“over”,“under”, “both”})
- 23. CLASSIFICATION Real life dataset Main hormone Long hair has_hotdog Sex estrogen 1 0 estrogen 1 0 testosterone 0 1 testosterone 0 0 estrogen 1 1 testosterone 0 1 testosterone 0 0
- 24. CLASSIFICATION Define object class Main hormone Long hair hotdog Sex estrogen 1 0 female estrogen 1 0 female testosterone 0 1 male testosterone 0 0 female estrogen 1 1 male testosterone 0 1 male testosterone 0 0 male
- 25. Regression Define object valuebased on other values Price depend on supply demand weekday time rush hour weather
- 26. Regression
- 27. Regression
- 28. Regression
- 29. Regression
- 31. How to test Boundaries values Outlier detection (anomaly detection) Use GANs
- 32. ML TASKS SUPERVISED: • Classification • Regression • Forecast UNSUPERVISED: • Clustering • Outlier detection • Dimensionality reduction
- 33. ML TASKS SUPERVISED: • Classification • Regression • Forecast UNSUPERVISED: • Clustering • Outlier detection • Dimensionality reduction
- 34. Clestering How to find classes? How many classes?
- 35. K-means
- 37. tittle text
- 38. X Y
- 39. X Y
- 40. X Y
- 41. X Y
- 42. X Y
- 43. X Y
- 44. X X Y
- 45. X Y
- 46. X Y
- 47. How to test Boundaries values Outlier detection (anomaly detection) Use GANs
- 50. Outlier detection Local anomaly Global anomaly Other cluster
- 51. Outlier detection library(dbscan) furniture_lof <- lof(scale(furniture), k = 5) Interpreting LOF LOF is a ratio of densities LOF > 1more likely to be anomalous LOF ≤ 1less likely to be anomalous Large LOF values indicate more isolated points
- 52. Outlier detection # Train deep autoencoder learning model on "normal" # training data, y ignored anomaly_model <- h2o.deeplearning( x = names(train_dataset), training_frame = train_dataset, activation = "Tanh", autoencoder = TRUE, hidden = c(50,20,50), sparse = TRUE, l1 = 1e-4, epochs = 100) # Compute reconstruction error with the Anomaly # detection app (MSE between output and input layers) Detected_anomalies<- h2o.anomaly(anomaly_model, test_dataset)
- 53. Outlier detection library(isofor) Isofor_model <- iForest(data = train_dataset, nt = 1) Isofor_score <- predict(Isofor_model, newdata = test_dataset)
- 55. Regression
- 56. text
- 57. Simpson Paradox
- 58. Regression
- 59. How to test Boundaries values Outlier detection (anomaly detection) Use GANs
- 60. GAN
- 61. GAN
- 62. GAN 0 1 0 1 1 1 0 1 0 1 0 А1 * А2 * А3 * А4
- 63. GAN 0 1 0 1 1 1 0 1 0 1 0 1 1 1 1 А1 * А2 * А3 * А4 Pooling
- 64. GAN
- 65. GAN – generated data
- 66. How to test Boundaries values Outlier detection (anomaly detection) Use GANs
- 67. ML testing OBJECT: ML App, Model, data, process SUBJECT: QA engineer, data analyst, data scientist, ML- engineer GOAL: find unexpected object behavior for improving object
- 68. What is ML applications errors Wrong: decision -- binary, multi class classification prediction -- regression, forecasting answer (generation) -- speech generation, picture generation, Not enough Accuracy (Precision and Recall): particular situation -- detecting (edges of) object (detect target on (medecine) battlefield) big amount of data -- ROC-AUC
- 69. Changes in testing philosophy Text Traditional software ML software Some FIXED expected results Sorted list for all situation one IN one OUT Some PROBABLE value Arranged list for particular situation multiple IN multiple OUT
- 70. Common data science mistakes •Cherry-Picking •Data Dredging •False Causality •Cobra Effect •Survivorship Bias •Gerrymandering •Sampling Bias •Gambler’s Fallacy link •Hawthorne Effect •Regression Fallacy •Simpson’s Paradox •McNamara Fallacy •Overfitting •Publishing Bias •Relying only on Summary Metrics (Anscombe )
- 71. What can be tested • Data • Feature • Entities • Model • Phases • Performance • Workflow
- 72. Application workflow UI Not ML part ML part Not ML part UI Interact with user gather data return data validate right answers inform user about errors possibility validate user knowledge for validating right or wrong answer
- 73. Application workflow UI Not ML part ML part Not ML part UI transform data integration with third party systems API actions form answers add business rules filtering and wrangling error handling outliers detection outliers handling missing data handling invalidation new rules with ML actions
- 74. Application workflow UI Not ML part ML part Not ML part UI Interact with user gather data return data validate right answers inform user about errors possibility validate user knowledge for validating right or wrong answer
- 75. Application workflow UI Not ML part ML part Not ML part UI Integrations end-to-end (system) reinforcement process new (absence) of data (rules) handling Require: big amount of data supervised different situations full automatization
- 76. QA engineer task: Interpret cases when application not work work not enough accurate work in non standard situation detect situation when application can damage others Gather data for taking decision interpret negative cases on outliers False positive False negative prepare special controversial data for validating system pictures with specific objects noise prepare controversial situation when application can generate errors test and research existing solutions (kaggle)
- 77. ML steps to reproduce: all entities which were wrong classified require understand why understand their cluster give possibility to detect them separately wrong measurement metric accuracy on big amount of data validate system with giving controversial data
- 78. BUG (issue) report Statuses: • does not work • wrong work • work not enough accurate • work not accurate • work not enough fast • WORK ON DEV SAMPLE
- 79. Who wants know more? If we collect at least 200 interested requests – We will create small course (smart talk or meetup series) for this. https://forms.gle/sYM1Rhc5MZXi76Di9
- 80. The end