Developer’s Guide

• Contributing
  • Submitting a bug report
  • Ways to contribute
  • Retrieving the latest code
  • Contributing code
    • How to contribute
    • Contributing pull requests
    • Filing Bugs
    • Issues for New Contributors
    • Documentation
    • Testing and improving test coverage
    • Developers web site
    • Issue Tracker Tags
  • Coding guidelines
    • Input validation
    • Random Numbers
    • Deprecation
    • Python versions supported
  • Code Review Guidelines
  • 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 and Tricks
  • Productivity and sanity-preserving tips
    • Viewing the rendered HTML documentation for a pull request
    • Folding and unfolding outdated diffs on pull requests
    • Checking out pull requests as remote-tracking branches
    • Display code coverage in pull requests
    • Useful pytest aliases and flags
  • Debugging memory errors in Cython with valgrind
• Utilities for Developers
  • Validation Tools
  • Efficient Linear Algebra & Array Operations
  • Efficient Random Sampling
  • Efficient Routines for Sparse Matrices
  • Graph Routines
    • 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++?
  • 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