Machine Learning With Scikit-Learn ODSC SF 2015

Introduction to machine learning with scikit-learn. Material at https://github.com/amueller/odscon-sf-2015

Transcript

1. Machine Learning with Scikit-Learn Andreas Mueller (NYU Center for Data Science, scikit-learn) Material: http://bit.ly/sklsf
2. 2 Me
3. 3 Classification Regression Clustering Semi-Supervised Learning Feature Selection Feature Extraction Manifold Learning Dimensionality Reduction Kernel Approximation Hyperparameter Optimization Evaluation Metrics Out-of-core learning …...
5. 5 Get the notebooks! http://bit.ly/sklsf
6. 6 http://scikit-learn.org/
7. 7 Hi Andy, I just received an email from the first tutorial speaker, presenting right before you, saying he's ill and won't be able to make it. I know you have already committed yourself to two presentations, but is there anyway you could increase your tutorial time slot, maybe just offer time to try out what you've taught? Otherwise I have to do some kind of modern dance interpretation of Python in data :-) -Leah Hi Andreas, I am very interested in your Machine Learning background. I work for X Recruiting who have been engaged by Z, a worldwide leading supplier of Y. We are expanding the core engineering team and we are looking for really passionate engineers who want to create their own story and help millions of people. Can we find a time for a call to chat for a few minutes about this? Thanks
8. 8 Hi Andy, I just received an email from the first tutorial speaker, presenting right before you, saying he's ill and won't be able to make it. I know you have already committed yourself to two presentations, but is there anyway you could increase your tutorial time slot, maybe just offer time to try out what you've taught? Otherwise I have to do some kind of modern dance interpretation of Python in data :-) -Leah Hi Andreas, I am very interested in your Machine Learning background. I work for X Recruiting who have been engaged by Z, a worldwide leading supplier of Y. We are expanding the core engineering team and we are looking for really passionate engineers who want to create their own story and help millions of people. Can we find a time for a call to chat for a few minutes about this? Thanks
9. 9 Doing Machine Learning With Scikit-Learn
10. 10 Representing Data X = 1.1 2.2 3.4 5.6 1.0 6.7 0.5 0.4 2.6 1.6 2.4 9.3 7.3 6.4 2.8 1.5 0.0 4.3 8.3 3.4 0.5 3.5 8.1 3.6 4.6 5.1 9.7 3.5 7.9 5.1 3.7 7.8 2.6 3.2 6.3
11. 11 Representing Data X = 1.1 2.2 3.4 5.6 1.0 6.7 0.5 0.4 2.6 1.6 2.4 9.3 7.3 6.4 2.8 1.5 0.0 4.3 8.3 3.4 0.5 3.5 8.1 3.6 4.6 5.1 9.7 3.5 7.9 5.1 3.7 7.8 2.6 3.2 6.3 one sample
12. 12 Representing Data X = 1.1 2.2 3.4 5.6 1.0 6.7 0.5 0.4 2.6 1.6 2.4 9.3 7.3 6.4 2.8 1.5 0.0 4.3 8.3 3.4 0.5 3.5 8.1 3.6 4.6 5.1 9.7 3.5 7.9 5.1 3.7 7.8 2.6 3.2 6.3 one sample one feature
13. 13 Representing Data X = y = 1.1 2.2 3.4 5.6 1.0 6.7 0.5 0.4 2.6 1.6 2.4 9.3 7.3 6.4 2.8 1.5 0.0 4.3 8.3 3.4 0.5 3.5 8.1 3.6 4.6 5.1 9.7 3.5 7.9 5.1 3.7 7.8 2.6 3.2 6.3 1.6 2.7 4.4 0.5 0.2 5.6 6.7 one sample one feature outputs / labels
14. 14 Training and Testing Data X = 1.1 2.2 3.4 5.6 1.0 6.7 0.5 0.4 2.6 1.6 2.4 9.3 7.3 6.4 2.8 1.5 0.0 4.3 8.3 3.4 0.5 3.5 8.1 3.6 4.6 5.1 9.7 3.5 7.9 5.1 3.7 7.8 2.6 3.2 6.3 y = 1.6 2.7 4.4 0.5 0.2 5.6 6.7
15. 15 Training and Testing Data X = 1.1 2.2 3.4 5.6 1.0 6.7 0.5 0.4 2.6 1.6 2.4 9.3 7.3 6.4 2.8 1.5 0.0 4.3 8.3 3.4 0.5 3.5 8.1 3.6 4.6 5.1 9.7 3.5 7.9 5.1 3.7 7.8 2.6 3.2 6.3 y = 1.6 2.7 4.4 0.5 0.2 5.6 6.7 training set test set
16. 16 Training and Testing Data X = 1.1 2.2 3.4 5.6 1.0 6.7 0.5 0.4 2.6 1.6 2.4 9.3 7.3 6.4 2.8 1.5 0.0 4.3 8.3 3.4 0.5 3.5 8.1 3.6 4.6 5.1 9.7 3.5 7.9 5.1 3.7 7.8 2.6 3.2 6.3 y = 1.6 2.7 4.4 0.5 0.2 5.6 6.7 training set test set from sklearn.cross_validation import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y)
17. 17 Supervised Machine Learning Training Data Training Labels Model
18. 18 Supervised Machine Learning Training Data Test Data Training Labels Model Prediction
19. 19 Supervised Machine Learning Training Data Test Data Training Labels Model Prediction Test Labels Evaluation
20. 20 Supervised Machine Learning Training Data Test Data Training Labels Model Prediction Test Labels Evaluation Training Generalization
21. 21 clf = RandomForestClassifier() clf.fit(X_train, y_train) Training Data Training Labels Model
22. 22 clf = RandomForestClassifier() clf.fit(X_train, y_train) Training Data Test Data Training Labels Model Prediction y_pred = clf.predict(X_test)
23. 23 clf = RandomForestClassifier() clf.fit(X_train, y_train) clf.score(X_test, y_test) Training Data Test Data Training Labels Model Prediction Test Labels Evaluation y_pred = clf.predict(X_test)
24. 24 IPython Notebook: Part 1 - Introduction to Scikit-learn
25. 25 Unsupervised Machine Learning Training Data Model
26. 26 Unsupervised Machine Learning Training Data Test Data Model New View
27. 27 pca = PCA() pca.fit(X_train) X_new = pca.transform(X_test) Training Data Test Data Model Transformation Unsupervised Transformations
28. 28 IPython Notebook: Part 2 – Unsupervised Transformers
29. 29 Basic API estimator.fit(X, [y]) estimator.predict estimator.transform Classification Preprocessing Regression Dimensionality reduction Clustering Feature selection Feature extraction
30. 30 All Data Training data Test data
31. 31 All Data Training data Test data Fold 1 Fold 2 Fold 3 Fold 4 Fold 5
32. 32 All Data Training data Test data Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Split 1
33. 33 All Data Training data Test data Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Split 1 Split 2
34. 34 All Data Training data Test data Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Split 1 Split 2 Split 3 Split 4 Split 5
35. 35 IPython Notebook: Part 3 - Cross-validation
38. 38 All Data Training data Test data
39. 39 All Data Training data Test data Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Test data Split 1 Split 2 Split 3 Split 4 Split 5
40. 40 All Data Training data Test data Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Test data Finding Parameters Final evaluation Split 1 Split 2 Split 3 Split 4 Split 5
41. 41 SVC(C=0.001, gamma=0.001)
42. 42 SVC(C=0.001, gamma=0.001) SVC(C=0.01, gamma=0.001) SVC(C=0.1, gamma=0.001) SVC(C=1, gamma=0.001) SVC(C=10, gamma=0.001)
43. 43 SVC(C=0.001, gamma=0.001) SVC(C=0.01, gamma=0.001) SVC(C=0.1, gamma=0.001) SVC(C=1, gamma=0.001) SVC(C=10, gamma=0.001) SVC(C=0.001, gamma=0.01) SVC(C=0.01, gamma=0.01) SVC(C=0.1, gamma=0.01) SVC(C=1, gamma=0.01) SVC(C=10, gamma=0.01)
44. 44 SVC(C=0.001, gamma=0.001) SVC(C=0.01, gamma=0.001) SVC(C=0.1, gamma=0.001) SVC(C=1, gamma=0.001) SVC(C=10, gamma=0.001) SVC(C=0.001, gamma=0.01) SVC(C=0.01, gamma=0.01) SVC(C=0.1, gamma=0.01) SVC(C=1, gamma=0.01) SVC(C=10, gamma=0.01) SVC(C=0.001, gamma=0.1) SVC(C=0.01, gamma=0.1) SVC(C=0.1, gamma=0.1) SVC(C=1, gamma=0.1) SVC(C=10, gamma=0.1)
45. 45 SVC(C=0.001, gamma=0.001) SVC(C=0.01, gamma=0.001) SVC(C=0.1, gamma=0.001) SVC(C=1, gamma=0.001) SVC(C=10, gamma=0.001) SVC(C=0.001, gamma=0.01) SVC(C=0.01, gamma=0.01) SVC(C=0.1, gamma=0.01) SVC(C=1, gamma=0.01) SVC(C=10, gamma=0.01) SVC(C=0.001, gamma=0.1) SVC(C=0.01, gamma=0.1) SVC(C=0.1, gamma=0.1) SVC(C=1, gamma=0.1) SVC(C=10, gamma=0.1) SVC(C=0.001, gamma=1) SVC(C=0.01, gamma=1) SVC(C=0.1, gamma=1) SVC(C=1, gamma=1) SVC(C=10, gamma=1) SVC(C=0.001, gamma=10) SVC(C=0.01, gamma=10) SVC(C=0.1, gamma=10) SVC(C=1, gamma=10) SVC(C=10, gamma=10)
46. 46 IPython Notebook: Part 4 – Grid Searches
47. 47 Training Data Training Labels Model
48. 48 Training Data Training Labels Model
49. 49 Training Data Training Labels Model Feature Extraction
50. 50 Training Data Training Labels Model Feature Extraction Scaling
51. 51 Training Data Training Labels Model Feature Extraction Scaling Feature Selection
52. 52 Training Data Training Labels Model Feature Extraction Scaling Feature Selection Cross Validation
53. 53 Training Data Training Labels Model Feature Extraction Scaling Feature Selection Cross Validation
54. 54 Pipelines
55. 55 Pipelines
56. 56 IPython Notebook: Part 5 - Preprocessing and Pipelines
57. 57 Do cross-validation over all steps jointly. Keep a separate test set until the very end.
58. 58 Sample application: Sentiment Analysis
59. 59 Review: One of the worst movies I've ever rented. Sorry it had one of my favorite actors on it (Travolta) in a nonsense role. In fact, anything made sense in this movie. Who can say there was true love between Eddy and Maureen? Don't you remember the beginning of the movie ? Is she so lovely? Ask her daughters. I don't think so. Label: negative Training data: 12500 positive, 12500 negative IMDB Movie Reviews Data
60. 60 Bag Of Word Representations CountVectorizer / TfidfVectorizer
61. 61 Bag Of Word Representations “This is how you get ants.” CountVectorizer / TfidfVectorizer
62. 62 Bag Of Word Representations “This is how you get ants.” ['this', 'is', 'how', 'you', 'get', 'ants'] CountVectorizer / TfidfVectorizer tokenizer
63. 63 Bag Of Word Representations “This is how you get ants.” ['this', 'is', 'how', 'you', 'get', 'ants'] CountVectorizer / TfidfVectorizer tokenizer Build a vocabulary over all documents ['aardvak', 'amsterdam', 'ants', ... 'you', 'your', 'zyxst']
64. 64 Bag Of Word Representations “This is how you get ants.” [0, …, 0, 1, 0, … , 0, 1 , 0, …, 0, 1, 0, …., 0 ] ants get you aardvak zyxst ['this', 'is', 'how', 'you', 'get', 'ants'] CountVectorizer / TfidfVectorizer tokenizer Sparse matrix encoding Build a vocabulary over all documents ['aardvak', 'amsterdam', 'ants', ... 'you', 'your', 'zyxst']
65. 65 IPython Notebook: Part 6 - Working With Text Data
66. 66 Feature Union Training Data Training Labels Model Feature Extraction I Feature Extraction II
67. 67 IPython Notebook: Part 7 – FeatureUnion
68. 68 Overfitting and Underfitting Model complexity Accuracy Training
69. 69 Overfitting and Underfitting Model complexity Accuracy Training Generalization
70. 70 Overfitting and Underfitting Model complexity Accuracy Training Generalization Underfitting Overfitting Sweet spot
71. 71 Linear SVM
72. 72 Linear SVM
73. 73 (RBF) Kernel SVM
74. 74 (RBF) Kernel SVM
75. 75 (RBF) Kernel SVM
76. 76 (RBF) Kernel SVM
77. 77 Decision Trees
78. 78 Decision Trees
79. 79 Decision Trees
80. 80 Decision Trees
81. 81 Decision Trees
82. 82 Decision Trees
83. 83 Random Forests
84. 84 Random Forests
85. 85 Random Forests
86. 86 Validation Curves train_scores, test_scores = validation_curve(SVC(), X, y, param_name="gamma", param_range=param_range)
87. Andreas Mueller 87 Scaling Up
88. Andreas Mueller 88 Three regimes of data • Fits in RAM • Fits on a Hard Drive • Doesn't fit on a single PC
89. Andreas Mueller 89 Three regimes of data • Fits in RAM (up to 256 GB?) • Fits on a Hard Drive (up to 6TB?) • Doesn't fit on a single PC
90. Andreas Mueller 90
91. Andreas Mueller 91
92. Andreas Mueller 92 "256Gb ought to be enough for anybody." - me
93. Andreas Mueller 93 "256Gb ought to be enough for anybody." - me (for machine learning)
94. Andreas Mueller 94 Subsample!
95. Andreas Mueller 95 The scikit-learn way
96. Andreas Mueller 96 HDD Network estimator.partial_fit(X_batch, y_batch) Your for-loop / polling Trained Scikit-learn estimator
97. 97 Supported Algorithms • All SGDClassifier derivatives • Naive Bayes • MinibatchKMeans • Birch • IncrementalPCA • MiniBatchDictionaryLearning
98. 98 IPython Notebook: Part 8 – Out Of Core Learning
99. 99 Stateless Transformers • Normalizer • HashingVectorizer • RBFSampler (and other kernel approx)
100. 100 Bag Of Word Representations “This is how you get ants.” [0, …, 0, 1, 0, … , 0, 1 , 0, …, 0, 1, 0, …., 0 ] ants get you aardvak zyxst ['this', 'is', 'how', 'you', 'get', 'ants'] CountVectorizer / TfidfVectorizer tokenizer Sparse matrix encoding Build a vocabulary over all documents ['aardvak', 'amsterdam', 'ants', ... 'you', 'your', 'zyxst']
101. 101 Hashing Trick “This is how you get ants.” [0, …, 0, 1, 0, … , 0, 1 , 0, …, 0, 1, 0, …., 0 ] ants get you aardvak zyxst ['this', 'is', 'how', 'you', 'get', 'ants'] HashingVectorizer tokenizer Sparse matrix encoding hashing [hash('this'), hash('is'), hash('how'), hash('you'), hash('get'), hash('ants')] = [832412, 223788, 366226, 81185, 835749, 173092]
102. 102 IPython Notebook: Part 9 – Out Of Core Learning for Text
103. 103 Video Series Advanced Machine Learning with scikit-learn 50% Off Coupon Code: AUTHD
104. 104 Video Series Advanced Machine Learning with scikit-learn 50% Off Coupon Code: AUTHD
105. 105 Thank you for your attention. @t3kcit @amueller importamueller@gmail.com http://amueller.github.io