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Practical Machine Learning and Rails Part2

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ryanstout

our machine learning talk, Ryan's part Part 1: http://www.slideshare.net/ryanstout/practical-machine-learning-and-rails-part1

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SENTIMENT CLASSIFICATION
Practical Machine Learning and Rails Part2
TRAINING DATA:
TRAINING DATA: - tweets
TRAINING DATA: - tweets - positive/negative
TRAINING DATA: - tweets - positive/negative - use emoticons from twitter
TRAINING DATA: - tweets - positive/negative - use emoticons from twitter :-) or :-(
BUILDING TRAINING DATA: NEGATIVE is upset that he cant update his Facebook by texting it... and might cry as a result School today also. Blah! I couldnt bear to watch it. And I thought the UA loss was embarrassing I hate when I have to call and wake people up POSITIVE Just woke up. Having no school is the best feeling ever Im enjoying a beautiful morning here in Phoenix dropping molly off getting ice cream with Aaron
Practical Machine Learning and Rails Part2
FEATURES:
FEATURES: BAG OF WORDS MODEL
FEATURES: BAG OF WORDS MODEL split the text into words, create a dictionary, and replace text with word counts
BAG OF WORDS
BAG OF WORDS tweets: I ran fast Bob ran far I ran to Bob
BAG OF WORDS tweets: I ran fast Bob ran far I ran to Bob dictionary = %w{I ran fast Bob far to}
BAG OF WORDS tweets: I ran fast Bob ran far I ran to Bob dictionary = %w{I ran fast Bob far to}
BAG OF WORDS tweets: word vectors: I ran fast [1 1 1 0 0 0] Bob ran far [0 1 0 1 1 0] I ran to Bob [1 1 0 1 0 1] dictionary = %w{I ran fast Bob far to}
CLASSIFIER:
CLASSIFIER: training examples: word vector -> labels
CLASSIFIER: training examples: word vector -> labels
CLASSIFIER: training examples: word vector -> labels classification algorithm
CLASSIFIER: training examples: word vector -> labels classification algorithm
CLASSIFIER: training examples: word vector -> labels classification algorithm model
WEKA
WEKA • open source java app
WEKA • open source java app • contains common ML algorithms
WEKA • open source java app • contains common ML algorithms • gui interface
WEKA • open source java app • contains common ML algorithms • gui interface • can access it from jruby
WEKA • open source java app • contains common ML algorithms • gui interface • can access it from jruby • helps with:
WEKA • open source java app • contains common ML algorithms • gui interface • can access it from jruby • helps with: • converting words into vectors
WEKA • open source java app • contains common ML algorithms • gui interface • can access it from jruby • helps with: • converting words into vectors • training/test, cross-validation, metrics
ARFF FILE
TRAINING IN WEKA [SHOW EXAMPLE HERE]
EVALUATION • correctly classified • mean squared error
EVALUATION false negative/positives
SENTIMENT CLASSIFICATION EXAMPLE https://github.com/ryanstout/ mlexample
QUERYING arff_path = Rails.root.join("data/sentiment.arff").to_s arff = FileReader.new(arff_path) model_path = Rails.root.join("models/sentiment.model").to_s classifier = SerializationHelper.read(model_path) data = begin Instances.new(arff,1).tap do |instance| if instance.class_index == -1 instance.set_class_index(instance.num_attributes - 1) end end end
QUERYING instance = SparseInstance.new(data.num_attributes) instance.set_dataset(data) instance.set_value(data.attribute(0), params[:sentiment][:message]) result = classifier.distribution_for_instance(instance).first percent_positive = 1 - result.to_f @message = "The text is #{(percent_positive*100.0).round}% positive"
HOW DO WE IMPROVE?
HOW DO WE IMPROVE? •bigger dictionary
HOW DO WE IMPROVE? •bigger dictionary •bi-grams/tri-grams
HOW DO WE IMPROVE? •bigger dictionary •bi-grams/tri-grams •part of speech tagging
HOW DO WE IMPROVE? •bigger dictionary •bi-grams/tri-grams •part of speech tagging •more data
Feature Generation
Feature Generation think about what information is valuable to an expert
Feature Generation think about what information is valuable to an expert remove data that isn't useful (attribute selection)
ATTRIBUTE SELECTION [SHOW ATTRIBUTE SELECTION EXAMPLE]
ATTRIBUTE SELECTION
DOMAIN PRICE PREDICTION • predict how much a domain would sell for
TRAINING DATA
TRAINING DATA • domains
TRAINING DATA • domains • historical sale prices for domains
FEATURES
FEATURES • split domain by words
FEATURES • split domain by words • generate features for each word
FEATURES • split domain by words • generate features for each word • how common the word is
FEATURES • split domain by words • generate features for each word • how common the word is • number of google results for each word
FEATURES • split domain by words • generate features for each word • how common the word is • number of google results for each word • cpc for the word
ALGORITHM support vector regression functions > SMOreg in weka
WHAT WE DIDN’T COVER
WHAT WE DIDN’T COVER • collaborative filtering
WHAT WE DIDN’T COVER • collaborative filtering • clustering
WHAT WE DIDN’T COVER • collaborative filtering • clustering • theorem proving (classical AI)
ADDITIONAL RESOURCES stanford machine learning class ml-class.org
TOOLS • weka • libsvm, liblinear • vowpal wabbit (big dictionaries) • recommendify • https://github.com/paulasmuth/recommendify
QUESTIONS contact us on twitter at @tectonic and @ryanstout

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Practical Machine Learning and Rails Part2

Editor's Notes

  1. having an example makes it easier to understand the process\n
  2. also could use movie/product review data\n
  3. also could use movie/product review data\n
  4. also could use movie/product review data\n
  5. also could use movie/product review data\n
  6. also could use movie/product review data\n
  7. \n
  8. bag of words - a way of generating features from text that only looks at which words occur in the text\n- doesn’t look at word order, syntax, grammar, punctuation, etc...\n
  9. bag of words - a way of generating features from text that only looks at which words occur in the text\n- doesn’t look at word order, syntax, grammar, punctuation, etc...\n
  10. bag of words - a way of generating features from text that only looks at which words occur in the text\n- doesn’t look at word order, syntax, grammar, punctuation, etc...\n
  11. words in dictionary array are replaced with the count’s in the text\n\n
  12. words in dictionary array are replaced with the count’s in the text\n\n
  13. words in dictionary array are replaced with the count’s in the text\n\n
  14. words in dictionary array are replaced with the count’s in the text\n\n
  15. word vectors/labels\n
  16. word vectors/labels\n
  17. word vectors/labels\n
  18. word vectors/labels\n
  19. word vectors/labels\n
  20. \n
  21. \n
  22. \n
  23. \n
  24. \n
  25. \n
  26. \n
  27. generated using RARFF\n
  28. \n
  29. \n
  30. \n
  31. \n
  32. load the arff\nload the model - serialized java object\nload a dataset\n
  33. create a sparse instance, set the dataset\nget distribution (predicted values for each class)\n
  34. the cat ran out the door\n[the cat] [cat ran] [ran out]...\n
  35. the cat ran out the door\n[the cat] [cat ran] [ran out]...\n
  36. the cat ran out the door\n[the cat] [cat ran] [ran out]...\n
  37. the cat ran out the door\n[the cat] [cat ran] [ran out]...\n
  38. \n
  39. \n
  40. \n
  41. \n
  42. \n
  43. \n
  44. \n
  45. assume a max of three words\neach feature of three words, 0’s if less words\n
  46. assume a max of three words\neach feature of three words, 0’s if less words\n
  47. assume a max of three words\neach feature of three words, 0’s if less words\n
  48. assume a max of three words\neach feature of three words, 0’s if less words\n
  49. assume a max of three words\neach feature of three words, 0’s if less words\n
  50. \n
  51. clustering - similar documents, related terms\n
  52. clustering - similar documents, related terms\n
  53. clustering - similar documents, related terms\n
  54. \n
  55. vowpal - good for large datasets, contains different algorithms (matrix factorization, collab filtering, lda, etc..)\n
  56. hopefully this helped you know the tools and techniques\nyou can teach yourself\nfeel free to contact us\n