Developer’s Guide

• Contributing
  • Submitting a bug report
  • Retrieving the latest code
  • Contributing code
    • How to contribute
    • Easy Issues
    • Documentation
    • Testing and improving test coverage
    • Developers web site
    • Issue Tracker Tags
  • Other ways to contribute
  • Coding guidelines
    • Input validation
    • Random Numbers
    • Deprecation
    • Python 3.x support
  • APIs of scikit-learn objects
    • Different objects
    • Estimators
      • Instantiation
      • Fitting
      • Estimated Attributes
      • Optional Arguments
  • Rolling your own estimator
    • get_params and set_params
    • Parameters and init
    • Cloning
    • Pipeline compatibility
    • Estimator types
    • Working notes
    • Specific models
• Developers’ Tips for Debugging
  • Memory errors: debugging Cython with valgrind
• Utilities for Developers
  • Validation Tools
  • Efficient Linear Algebra & Array Operations
  • Efficient Random Sampling
  • Efficient Routines for Sparse Matrices
  • Graph Routines
  • Backports
    • ARPACK
    • Benchmarking
  • Testing Functions
  • Multiclass and multilabel utility function
  • Helper Functions
  • Hash Functions
  • Warnings and Exceptions
• How to optimize for speed
  • Python, Cython or C/C++?
  • Fast matrix multiplications
  • Profiling Python code
  • Memory usage profiling
  • Performance tips for the Cython developer
  • Profiling compiled extensions
    • Using yep and google-perftools
    • Using gprof
    • Using valgrind / callgrind / kcachegrind
  • Multi-core parallelism using joblib.Parallel
  • A sample algorithmic trick: warm restarts for cross validation
• Advanced installation instructions
  • Installing an official release
    • Mac OSX
    • Linux
      • installing build dependencies
      • building scikit-learn with pip
      • from source package
    • windows
  • third party distributions of scikit-learn
    • macports for mac osx
    • arch linux
    • netbsd
    • fedora
    • building on windows
    • 32-bit python
    • 64-bit python
    • building binary packages and installers
    • using an alternative compiler
  • bleeding edge
  • testing
    • testing scikit-learn once installed
    • testing scikit-learn from within the source folder
• Maintainer / core-developer information
  • Making a release