MILK: MACHINE LEARNING TOOLKIT
Machine Learning in Python
Milk is a machine learning toolkit in Python.
Its focus is on supervised classification with several classifiers available: SVMs (based on libsvm), k-NN, random forests, decision trees. It also performs feature selection. These classifiers can be combined in many ways to form different classification systems.
For unsupervised learning, milk supports k-means clustering and affinity propagation.
Milk is flexible about its inputs. It optimised for numpy arrays, but can often handle anything (for example, for SVMs, you can use any dataype and any kernel and it does the right thing).
There is a strong emphasis on speed and low memory usage. Therefore, most of the performance sensitive code is in C++. This is behind Python-based interfaces for convenience.
To learn more, check the docs at http://packages.python.org/milk/ or the code demos included with the source
New in 0.3.9
assign_centroidfunction in milk.unsupervised.nfoldcrossvalidation
- Improve speed of k-nearest neighbour (10x on scikits-learn benchmark)
- Improve kmeans on newer numpy (works for larger datasets too)
- Faster kmeans by coding centroid recalculation in C++
- Fix gridminize for low count labels
- Fix bug with non-integer labels for tree learning
New in 0.3.8
- Fix compilation on Windows
New in 0.3.7
- Logistic regression
- Source demos included (in source and documentation)
- Add cluster agreement metrics
- Fix nfoldcrossvalidation bug when using origins
New in 0.3.6
- Unsupervised (1-class) kernel density modeling
- Fix for when SDA returns empty
- weights option to some learners
- stump learner
- Adaboost (result of above changes)
New in 0.3.5
- Fixes for 64 bit machines
- Functions in measures.py all have same interface now.
New in 0.3.4
- Random forest learners
- Decision trees sped up 20x
- Much faster gridsearch (finds optimum without computing all folds)
- Random forests
- Self organising maps
- SVMs. Using the libsvm solver with a pythonesque wrapper around it.
- Stepwise Discriminant Analysis for feature selection.
- Non-negative matrix factorisation
- K-means using as little memory as possible.
- Affinity propagation