PyTexas 2015 ML Tutorial

Beginner's Guide to Machine Learning Competitions, PyTexas

1. Beginner’s guide to Machine Learning competitions Christine Doig PyTexas 2015
2. Christine Doig Data Scientist, Continuum Analytics ch_doig chdoig chdoig.github.io
3. Data Science Machine Learning Supervised learning Classification Kaggle Competitions Dataset Setup Feature preparation Modeling Optimization Validation Anaconda Concepts Process NLP Sentiment analysis
4. Data Science Machine Learning Supervised learning Classification Concepts NLP Sentiment analysis
5. Data Science
7. Slides: http://www.slideshare.net/joshwills/production-machine-learninginfrastructure Video: https://www.youtube.com/watch?v=v-91JycaKjc From the lab to the factory - Data Day Texas data science http://www.oreilly.com/data/free/files/analyzing-the-analyzers.pdf http://www.experfy.com/blog/become-data-scientist/ http://www.fico.com/landing/infographic/anatomy-of-a-data-scientist_en.html
8. data science Scientific Computing Distributed Systems Analytics Machine Learning/Stats Web
9. data science Scientific Computing Distributed Systems Analytics Machine Learning/Stats Web Data Scientists/ Modeler Data/Business Analyst Research/Computational Scientist Data Engineers/ Architects Developer
10. data science Scientific Computing Distributed Systems Analytics Machine Learning/Stats Web Data Scientists/ Modeler Data/Business Analyst Research/Computational Scientist Data Engineers/ Architects Developer Model Algorithm Report Application Pipeline/ Architecture
11. data science Scientific Computing Distributed Systems Analytics Machine Learning/Stats Web Data Scientists/ Modeler Data/Business Analyst Research/Computational Scientist Data Engineers/ Architects Models Deep Learning Supervised Clustering SVM Regression Classification Crossvalidation Dimensionality reduction KNN Unsupervised NN Filter Join Select TopK Sort Groupby min summary statistics avg max databases GPUs arrays algorithms performance compute SQL Reporting clusters hadoop hdfs optimization HPC graphs FTT HTML/CSS/JS algebra stream processing deployment servers frontend sematic web batch jobs consistency A/B testing crawling frameworks parallelism availability tolerance DFT spark scraping databases apps NOSQL parallelism interactive data viz pipeline cloud Developer
12. data science Scientific Computing Distributed Systems Analytics Machine Learning/Stats Web Data Scientists/ Modeler Data/Business Analyst Research/Computational Scientist Data Engineers/ Architects Developer PyMC Numba xlwings Bokeh Kafka RDFLib mrjob mrjob
13. Machine Learning
15. Machine Learning Unsupervised learning Supervised learning Classification Regression Clustering Latent variables/structure labels no labels categorical quantitative Linear regression Logistic regression SVM Decision trees k-NN K-means Hierarchical clustering *Topic modeling Dimenstionality reduction *Topic modeling
16. Exploratory Predictive Machine Learning Unsupervised learning Supervised learning Classification Regression Clustering Latent variables/structure labels no labels categorical quantitative Linear regression Logistic regression SVM Decision trees k-NN K-means Hierarchical clustering *Topic modeling Dimenstionality reduction *Topic modeling
17. Exploratory Predictive Machine Learning Unsupervised learning Supervised learning Classification Regression labels no labels categorical quantitative id gender age job_id 1 F 67 1 2 M 32 2 3 M 45 1 4 F 18 2 group similar individuals together id gender age job_id buy/click_ad money_spent 1 F 67 1 Yes $1,000 2 M 32 2 No - 3 M 45 1 No - 4 F 18 2 Yes $300 predict whether an individual is going to buy/click or not Classification Regression predict how much is the individual going to spend
18. Natural Language Processing
19. I love you! Positive
20. Machine Learning Natural language processing field concerned with the interactions between computers and human (natural) languages Sentiment analysis Extract subjective information on polarity (positive or negative) of a document (text, tweet, voice message…) ! e.g online reviews to determine how people feel about a particular object or topic. tasks
21. Machine Learning Unsupervised learning Supervised learning Classification Regression Clustering Latent variables/structure labels no labels categorical quantitative Linear regression Logistic regression SVM Decision trees k-NN K-means Hierarchical clustering *Topic modeling Dimenstionality reduction *Topic modeling Sentiment analysis Movie review Positive Negative e.g.
22. Setup
23. Kaggle Competitions Dataset Setup Anaconda
24. Setup options You already have Python installed and your own workflow to install Python packages happy alternative Install dependencies in README Anaconda Miniconda + conda env Free Python distribution with a bunch of packages for data science too many packages!!! Python + conda (package manager) git clone git@github.com:chdoig/pytexas2015-ml.git cd pytexas2015-ml conda env create source activate pytexas-ml http://conda.pydata.org/miniconda.html http://continuum.io/downloads Python + conda (package manager) + packages
25. Kaggle https://www.kaggle.com/ hosts online machine learning competitions
26. Kaggle Competition https://www.kaggle.com/c/word2vec-nlp-tutorial
27. Kaggle Competition https://www.kaggle.com/c/word2vec-nlp-tutorial Bag of Words Meets Bags of Popcorn Data T ask 50,000 IMDB movie reviews predict the sentiment for each review in the test data set 25,000 rows containing an id, sentiment, and text for each review. labeledTrainData.tsv testData.tsv 25,000 rows containing an id and text for each review
28. Feature preparation Modeling Optimization Validation Process
29. Feature preparation Modeling Optimization Validation Feature extraction Feature selection Feature imputation Feature scaling Feature discretization Neural Networks Decision trees Random forest SVM Naive Bayes classifier Logistic Regression Boosting Bagging Regularization Hold out method Crossvalidation Confusion matrix ROC curve / AUC Hyperparameters Machine Learning Ensemble
30. Feature preparation Feature extraction the process of making features from available data to be used by the classification algorithms Reviews M N Words Model Evaluation Metrics Visualizations NaiveBayes DecisionT rees Feature extraction id sentiment review count_words terrible_word 1 0 the movie was terrible 4 1 2 1 I love it 3 0 3 1 Awesome! Love it! 3 0 4 0 I hated every minute 4 0
31. T okenization Stopwords transition, metal, oxides, considered, generation, materials, field, electronics, advanced, catalysts, tantalum, v, oxide, reports, synthesis, material, nanometer, size, unusual, properties… transition_metal_oxides, considered, generation, materials, field, electronics, advanced, catalysts, tantalum, oxide, reports, synthesis, material, nanometer_size, unusual, properties, sol_gel_method, biomedical_applications… transition, metal_oxides, tantalum, oxide, nanometer_size, unusual_properties, dna, easy_method, biomedical_applications transition, metal_oxides, generation, tantalum, oxide, nanometer_size, unusual_properties, sol, dna, easy_method, biomedical_applications Simple Collocations Entities Combination Lemmatization transition, metal, oxide, consider, generation, material, field, electronic, advance, catalyst, property… language generic domain specific a! above! across! after! afterwards! again! against! all ! … material! temperature! advance! size! …. Feature extraction Text
32. Vector Space Corpus - Bag of words Dictionary 1 - transition! 2- metal! 3- oxides! 4- considered! … ! [(0, 1), (1, 1), (2, 1)] [(0, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1)] [(2, 1), (5, 1), (7, 1), (8, 1)] [(1, 1), (5, 2), (8, 1)] [(3, 1), (6, 1), (7, 1)] [(9, 1)] [(9, 1), (10, 1)] [(9, 1), (10, 1), (11, 1)] [(4, 1), (10, 1), (11, 1)]
33. Feature_extraction.ipynb
34. Feature preparation Modeling Optimization Validation Feature extraction Feature selection Feature imputation Feature scaling Feature discretization Neural Networks Decision trees Random forest SVM Naive Bayes classifier Logistic Regression Boosting Bagging Regularization Hold out method Crossvalidation Confusion matrix ROC curve / AUC Hyperparameters Machine Learning Ensemble
35. Modeling Naive Bayes Classifier P(A|B) = P(B|A) * P(A) / P(B) id sentiment review count_words terrible_word 1 0 the movie was terrible 4 1 2 1 I love it 3 0 3 1 Awesome! Love it 3 0 4 0 I hated every minute 4 0 P(1 | love) = P(love | 1) * P(1) / P(love) = (2/2 * 2/4)/(2/4) = 100% What’s the probability of the review being positive if the word love appears in the review?
36. Modeling.ipynb
37. Feature preparation Modeling Optimization Validation Feature extraction Feature selection Feature imputation Feature scaling Feature discretization Neural Networks Decision trees Random forest SVM Naive Bayes classifier Logistic Regression Boosting Bagging Regularization Hold out method Crossvalidation Confusion matrix ROC curve / AUC Hyperparameters Machine Learning Ensemble
38. occurs whenever a model learns from patterns that are present in the training data but do not reflect the data-generating process. Seeing more than is actually there. A kind of data hallucination. Validation Overfitting http://talyarkoni.org/downloads/ML_Meetup_Yarkoni_Overfitting.pdf
39. T raining data Validation Model Evaluate New data Evaluate
40. Validation.ipynb
41. Validation Hold out method T raining data T est data accuracy
42. Crossvalidation Test Training ! + ! Validation Training Validation Accuracy = average(Round1, Round 2….) Final Accuracy one shot at this! Accuracy in each round with validation set
43. Confusion matrix Validation Positive reviews Negative reviews 95% 5% Accuracy 95% Real Model prediction
44. model/real positive negative positive 95 5 negative 0 0 Confusion matrix Validation
45. ROC curve/ AUC Validation true positive false positive 100% true positive 0 % false positive
46. ROC curve/ AUC Validation true positive false positive 100% true positive 0 % false positive AUC
47. Kaggle leaderboard
48. Feature preparation Modeling Optimization Validation Feature extraction Feature selection Feature imputation Feature scaling Feature discretization Neural Networks Decision trees Random forest SVM Naive Bayes classifier Logistic Regression Boosting Bagging Regularization Hold out method Crossvalidation Confusion matrix ROC curve / AUC Hyperparameters Machine Learning Ensemble
49. Optimization Ensemble methods Classifier 1 Classifier 2 Classifier 3 id cls_1 cls_2 cls_3 ensemble 1 0 0 0 0 2 0 1 1 1 3 1 1 1 1 4 0 0 1 0 e.g. majority voting w1 w2 w3 e.g. weighted voting
50. Kaggle forums
51. Data Science Machine Learning Supervised learning Classification Kaggle Competitions Dataset Setup Feature preparation Modeling Optimization Validation Anaconda Concepts Process NLP Sentiment analysis
52. Feature preparation Modeling Optimization Validation Feature extraction Feature selection Feature imputation Feature scaling Feature discretization Neural Networks Decision trees Random forest SVM Naive Bayes classifier Logistic Regression Boosting Bagging Regularization Hold out method Crossvalidation Confusion matrix ROC curve / AUC Hyperparameters Machine Learning Ensemble