MLSEV. Association Discovery and Topic Modeling
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1. Topic modeling is an unsupervised machine learning technique that analyzes documents to discover hidden topics. 2. It outputs a set of topics, where each topic is represented as a collection of related words. 3. It also assigns probabilities to topics for each document, indicating how strongly the document relates to different topics.
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MLSEV. Association Discovery and Topic Modeling
- 1. 1st edition March 7-8, 2019
- 2. BigML, Inc X#MLSEV: Association Discovery Association Discovery Rule-Based Machine Learning Charles Parker VP Machine Learning Algorithms
- 3. BigML, Inc X#MLSEV: Association Discovery Association Discovery An unsupervised learning technique • No labels necessary • Useful for data discovery Finds "significant" correlations/associations/relations • Shopping cart: Coffee and sugar • Medical: High plasma glucose and diabetes Expresses them as "if then rules" • If "antecedent" then "consequent"
- 4. BigML, Inc X#MLSEV: Association Discovery Review of methods: clustering date customer account auth class zip amount Mon Bob 3421 pin clothes 46140 135 Tue Bob 3421 sign food 46140 401 Tue Alice 2456 pin food 12222 234 Wed Sally 6788 pin gas 26339 94 Wed Bob 3421 pin tech 21350 2459 Wed Bob 3421 pin gas 46140 83 Thr Sally 6788 sign food 26339 51
- 5. BigML, Inc X#MLSEV: Association Discovery Review of methods: clustering date customer account auth class zip amount Mon Bob 3421 pin clothes 46140 135 Tue Bob 3421 sign food 46140 401 Tue Alice 2456 pin food 12222 234 Wed Sally 6788 pin gas 26339 94 Wed Bob 3421 pin tech 21350 2459 Wed Bob 3421 pin gas 46140 83 Thr Sally 6788 sign food 26339 51 similar
- 6. BigML, Inc X#MLSEV: Association Discovery Review: anomaly detection date customer account auth class zip amount Mon Bob 3421 pin clothes 46140 135 Tue Bob 3421 sign food 46140 401 Tue Alice 2456 pin food 12222 234 Wed Sally 6788 pin gas 26339 94 Wed Bob 3421 pin tech 21350 2459 Wed Bob 3421 pin gas 46140 83 Thr Sally 6788 sign food 26339 51
- 7. BigML, Inc X#MLSEV: Association Discovery Review: anomaly detection date customer account auth class zip amount Mon Bob 3421 pin clothes 46140 135 Tue Bob 3421 sign food 46140 401 Tue Alice 2456 pin food 12222 234 Wed Sally 6788 pin gas 26339 94 Wed Bob 3421 pin tech 21350 2459 Wed Bob 3421 pin gas 46140 83 Thr Sally 6788 sign food 26339 51 anomaly
- 8. BigML, Inc X#MLSEV: Association Discovery Association Discovery date customer account auth class zip amount Mon Bob 3421 pin clothes 46140 135 Tue Bob 3421 sign food 46140 401 Tue Alice 2456 pin food 12222 234 Wed Sally 6788 pin gas 26339 94 Wed Bob 3421 pin tech 21350 2459 Wed Bob 3421 pin gas 46140 83 Thr Sally 6788 sign food 26339 51
- 9. BigML, Inc X#MLSEV: Association Discovery Association Discovery date customer account auth class zip amount Mon Bob 3421 pin clothes 46140 135 Tue Bob 3421 sign food 46140 401 Tue Alice 2456 pin food 12222 234 Wed Sally 6788 pin gas 26339 94 Wed Bob 3421 pin tech 21350 2459 Wed Bob 3421 pin gas 46140 83 Thr Sally 6788 sign food 26339 51 {customer = Bob, account = 3421}
- 10. BigML, Inc X#MLSEV: Association Discovery Association Discovery date customer account auth class zip amount Mon Bob 3421 pin clothes 46140 135 Tue Bob 3421 sign food 46140 401 Tue Alice 2456 pin food 12222 234 Wed Sally 6788 pin gas 26339 94 Wed Bob 3421 pin tech 21350 2459 Wed Bob 3421 pin gas 46140 83 Thr Sally 6788 sign food 26339 51 zip = 46140{customer = Bob, account = 3421}
- 11. BigML, Inc X#MLSEV: Association Discovery Association Discovery date customer account auth class zip amount Mon Bob 3421 pin clothes 46140 135 Tue Bob 3421 sign food 46140 401 Tue Alice 2456 pin food 12222 234 Wed Sally 6788 pin gas 26339 94 Wed Bob 3421 pin tech 21350 2459 Wed Bob 3421 pin gas 46140 83 Thr Sally 6788 sign food 26339 51 zip = 46140{customer = Bob, account = 3421} {class = gas}
- 12. BigML, Inc X#MLSEV: Association Discovery Association Discovery date customer account auth class zip amount Mon Bob 3421 pin clothes 46140 135 Tue Bob 3421 sign food 46140 401 Tue Alice 2456 pin food 12222 234 Wed Sally 6788 pin gas 26339 94 Wed Bob 3421 pin tech 21350 2459 Wed Bob 3421 pin gas 46140 83 Thr Sally 6788 sign food 26339 51 zip = 46140 amount < 100 {customer = Bob, account = 3421} {class = gas}
- 13. BigML, Inc X#MLSEV: Association Discovery Association Discovery date customer account auth class zip amount Mon Bob 3421 pin clothes 46140 135 Tue Bob 3421 sign food 46140 401 Tue Alice 2456 pin food 12222 234 Wed Sally 6788 pin gas 26339 94 Wed Bob 3421 pin tech 21350 2459 Wed Bob 3421 pin gas 46140 83 Thr Sally 6788 sign food 26339 51 zip = 46140 amount < 100 Rules: Antecedent Consequent {customer = Bob, account = 3421} {class = gas}
- 14. BigML, Inc X#MLSEV: Association Discovery Use Cases • Data Discovery: how do instances relate? • Market Basket Analysis: Items that go together • Behaviors that occur together • Web usage patterns • Intrusion detection • Fraud detection • Medical risk factors
- 15. BigML, Inc X#MLSEV: Association Discovery Association Metrics • Coverage • Support • Confidence • Lift • Leverage Associations between grocery items
- 16. BigML, Inc X#MLSEV: Association Discovery Association Metrics: coverage Coverage Percentage of instances which match antecedent “A” Instances A C
- 17. BigML, Inc X#MLSEV: Association Discovery Association Metrics: support Instances A C Support Percentage of instances which match antecedent “A” and Consequent “C”
- 18. BigML, Inc X#MLSEV: Association Discovery Confidence Percentage of instances in the antecedent which also contain the consequent. Association Metrics: confidence Coverage Support Instances A C
- 19. BigML, Inc X#MLSEV: Association Discovery Association Metrics: confidence C Instances A C A Instances C Instances A Instances A C 0% 100% Instances A C Confidence A never implies C A sometimes implies C A always implies C A >> C A = C A << C
- 20. BigML, Inc X#MLSEV: Association Discovery Association Metrics: lift Lift Ratio of observed support to support if A and C were statistically independent. Support == Confidence p(A) * p(C) p(C) Independent A C C Observed A Problem: if p(C) is "small" then… lift may be large.
- 21. BigML, Inc X#MLSEV: Association Discovery Association Metrics: lift C Observed A Observed A C < 1 > 1 Independent A C Lift = 1 Negative Correlation No Correlation Positive Correlation Independent A C Independent A C Observed A C
- 22. BigML, Inc X#MLSEV: Association Discovery Association Metrics: leverage Leverage Difference of observed support and support if A and C were statistically independent. Support - [ p(A) * p(C) ] Independent A C C Observed A
- 23. BigML, Inc X#MLSEV: Association Discovery Association Metrics: leverage C Observed A Observed A C < 0 > 0 Independent A C Leverage = 0 Negative Correlation No Correlation Positive Correlation Independent A C Independent A C Observed A C -1…
- 24. BigML, Inc X#MLSEV: Association Discovery Items Type itemscoffee, sugar, milk, honey, dish soap, bread items • Canonical example: shopping cart contents • Single feature describing a list of items • Each item separated by a comma (default)
- 25. BigML, Inc X#MLSEV: Association Discovery Use Cases GOAL: Discover “interesting” rules about what store items are typically purchased together. • Dataset of 9,834 grocery cart transactions • Each row is a list of all items in a cart at checkout
- 26. BigML, Inc X#MLSEV: Association Discovery Association Demo
- 27. BigML, Inc X#MLSEV: Association Discovery Summary • Unsupervised learning technique for discovering interesting associations • Outputs antecedent/consequent rules • Metrics: Support / Coverage / Confidence / Lift / Leverage • Useful for “items” type and market basket analysis • Applicable to understanding clusters and anomaly detectors
- 28. BigML, Inc X#MLSEV: Topic Models Topic Models “One of these things is not like the other things . . . “ Charles Parker VP Machine Learning Algorithms
- 29. BigML, Inc X#MLSEV: Topic Models What is Topic Modeling? • Unsupervised algorithm • Learns only from text fields • Finds hidden topics that model the text Text Fields • How is this different from the Text Analysis that BigML already offers? • What does it output and how do we use it? Questions:
- 30. BigML, Inc X#MLSEV: Topic Models What is Topic Modeling? • Finds topics in your text fields • A topic is a distribution over terms • Terms with high probability in the same topic often occur together in the same document • Topics often correspond to real-world things that the document may be “about” (e.g., sports, cooking, technology) • Each document is “about” one or more topics • Usually each document is only about one or two topics • But in practice we assign a probability to every topic for every document
- 31. BigML, Inc X#MLSEV: Topic Models Text Analysis Be not afraid of greatness: some are born great, some achieve greatness, and some have greatness thrust upon 'em. great: appears 4 times 1. Stem Words -> Tokens 2. Remove tokens that occur too often 3. Remove tokens that do not occur often enough 4. Count occurrences of remaining “interesting” tokens
- 32. BigML, Inc X#MLSEV: Topic Models Text Analysis Be not afraid of greatness: some are born great, some achieve greatness, and some have greatness thrust upon ‘em. … great afraid born achieve … … … 4 1 1 1 … … … … … … … … … Model The token “great” occurs more than 3 times The token “afraid” occurs no more than once
- 33. BigML, Inc X#MLSEV: Topic Models Text Analysis
- 34. BigML, Inc X#MLSEV: Topic Models Hodor!
- 35. BigML, Inc X#MLSEV: Topic Models Text Analysis vs. Topic Modeling Text Topic Model Creates thousands of hidden token counts Token counts are independently uninteresting No semantic importance Co-occurrence limited to consecutive n-grams Creates tens of topics that model the text Topics are independently interesting Semantic meaning extracted Topics indicate broader co-occurrences
- 36. BigML, Inc X#MLSEV: Topic Models Generating Documents cat shoe zebra ball tree jump pen asteroid cable box step cabinet yellow plate flashlight… shoe asteroid flashlight pizza… plate giraffe purple jump… Be not afraid of greatness: some are born great, some achieve greatness… • "Machine" that generates a random word with equal probability with each pull. • Pull random number of times to generate a document. • All documents can be generated, but most are nonsense. word probability shoe ϵ asteroid ϵ flashlight ϵ pizza ϵ … ϵ
- 37. BigML, Inc X#MLSEV: Topic Models Topic Model • Written documents have meaning - one way to describe meaning is to assign a topic. • For our random machine, the topic can be thought of as increasing the probability of certain words. Intuition: Topic: travel cat shoe zebra ball tree jump pen asteroid cable box step cabinet yellow plate flashlight… airplane passport pizza … word probability travel 23,55 % airplane 2,33 % mars 0,003 % mantle ϵ … ϵ Topic: space cat shoe zebra ball tree jump pen asteroid cable box step cabinet yellow plate flashlight… mars quasar lightyear soda word probability space 38,94 % airplane ϵ mars 13,43 % mantle 0,05 % … ϵ
- 38. BigML, Inc X#MLSEV: Topic Models Topic Model plate giraffe purple jump… Topic: "1" cat shoe zebra ball tree jump pen asteroid cable box step cabinet yellow plate flashlight… word probability travel 23,55 % airplane 2,33 % mars 0,003 % mantle ϵ … ϵ Topic: "k" cat shoe zebra ball tree jump pen asteroid cable box step cabinet yellow plate flashlight… word probability shoe 12,12 % coffee 3,39 % telephone 13,43 % paper 4,11 % … ϵ …Topic: "2" cat shoe zebra ball tree jump pen asteroid cable box step cabinet yellow plate flashlight… word probability space 38,94 % airplane ϵ mars 13,43 % mantle 0,05 % … ϵ airplane passport pizza … plate giraffe purple jump… • Each text field in a row is concatenated into a document • The documents are analyzed to generate "k" related topics • Each topic is represented by a distribution of term probabilities
- 39. BigML, Inc X#MLSEV: Topic Models Training Topic Models
- 40. BigML, Inc X#MLSEV: Topic Models Topic Distribution • Any given document is likely a mixture of the modeled topics… • This can be represented as a distribution of topic probabilities Intuition: Will 2020 be the year that humans will embrace space exploration and finally travel to Mars? Topic: travel cat shoe zebra ball tree jump pen asteroid cable box step cabinet yellow plate flashlight… word probability travel 23,55 % airplane 2,33 % mars 0,003 % mantle ϵ … ϵ 11% Topic: space cat shoe zebra ball tree jump pen asteroid cable box step cabinet yellow plate flashlight… word probability space 38,94 % airplane ϵ mars 13,43 % mantle 0,05 % … ϵ 89%
- 41. BigML, Inc X#MLSEV: Topic Models Topic Distributions
- 42. BigML, Inc X#MLSEV: Topic Models Prediction? Unlabelled Data Centroid Label Unlabelled Data topic 1 prob topic 3 prob topic k prob Clustering Batch Centroid Topic Model Text Fields Batch Topic Distribution …
- 43. BigML, Inc X#MLSEV: Topic Models Topic Model Use Cases • As a preprocessor for other techniques • Building better models • Bootstrapping categories for classification • Recommendation • Discovery in large, heterogeneous text datasets