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GeezerVison is showing scikit-learn.org — Developer’s Guide

Developer’s Guide#

  • Contributing
    • Ways to contribute
      • New Contributors
    • Automated Contributions Policy
    • Licensing your contribution
    • Submitting a bug report or a feature request
      • How to make a good bug report
    • Contributing code and documentation
      • Development workflow
      • Pull request checklist
      • Continuous Integration (CI)
        • Commit message markers
        • Resolve conflicts in lock files
      • Stalled pull requests
      • Stalled and Unclaimed Issues
      • Issues tagged “Needs Triage”
      • Video resources
    • Documentation
      • Building the documentation
      • Generated documentation on GitHub Actions
    • Testing and improving test coverage
    • Monitoring performance
    • Issue Tracker Tags
    • Maintaining backwards compatibility
      • Deprecation
      • Change the default value of a parameter
    • Code Review Guidelines
    • Reading the existing code base
  • Set up your development environment
    • Fork the scikit-learn repository
    • Set up a local clone of your fork
    • Set up a dedicated environment and install dependencies
    • Install editable version of scikit-learn
    • Set up pre-commit
  • Global configuration and environment variables
    • Performance and memory
      • SKLEARN_ASSUME_FINITE
      • SKLEARN_WORKING_MEMORY
      • SKLEARN_PAIRWISE_DIST_CHUNK_SIZE
    • Testing and CI
      • SKLEARN_SEED
      • SKLEARN_TESTS_GLOBAL_RANDOM_SEED
      • SKLEARN_SKIP_NETWORK_TESTS
      • SKLEARN_RUN_FLOAT32_TESTS
      • SKLEARN_WARNINGS_AS_ERRORS
    • Build and debug
      • SKLEARN_ENABLE_DEBUG_CYTHON_DIRECTIVES
      • SKLEARN_SKIP_OPENMP_TEST
      • SKLEARN_TEST_NO_OPENMP
  • Crafting a minimal reproducer for scikit-learn
    • Good practices
      • Provide a failing code example with minimal comments
      • Boil down your script to something as small as possible
      • DO NOT report your data unless it is extremely necessary
      • Use markdown formatting
    • Synthetic dataset
      • NumPy
      • Pandas
      • make_regression
      • make_classification
      • make_blobs
      • Dataset loading utilities
  • Developing scikit-learn estimators
    • APIs of scikit-learn objects
      • Different objects
      • Estimators
        • Instantiation
        • Fitting
        • Estimated Attributes
        • Universal attributes
    • Rolling your own estimator
      • get_params and set_params
      • Cloning
      • Estimator types
      • Estimator Tags
    • Developer API for set_output
    • Developer API for check_is_fitted
    • Developer API for HTML representation
    • Coding guidelines
      • Input validation
      • Random Numbers
      • Numerical assertions in tests
  • Developers’ Tips and Tricks
    • Productivity and sanity-preserving tips
      • 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
      • Standard replies for reviewing
      • Debugging CI issues
        • Using a lock-file to get an environment close to the CI
    • Debugging memory errors in Cython with valgrind
    • Building and testing for the ARM64 platform on an x86_64 machine
    • The Meson Build Backend
  • Utilities for Developers
    • Validation Tools
    • Efficient Linear Algebra & Array Operations
    • Efficient Random Sampling
    • Efficient Routines for Sparse Matrices
    • Graph Routines
    • 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
      • Peak memory
      • Memory leaks
    • Using Cython
    • Profiling compiled extensions
      • Using yep and gperftools
      • Using a debugger, gdb
      • Using gprof
      • Using valgrind / callgrind / kcachegrind
        • kcachegrind
    • Multi-core parallelism using joblib.Parallel
    • A simple algorithmic trick: warm restarts
  • Cython Best Practices, Conventions and Knowledge
    • Tips for developing with Cython in scikit-learn
      • Tips to ease development
      • Tips for performance
      • Using OpenMP
        • Types
  • Miscellaneous information / Troubleshooting
    • Notes on OpenMP
    • Notes on Conda
    • Note on dependencies for other Linux distributions
    • Notes on Meson
  • Bug triaging and issue curation
    • Working on issues to improve them
    • Working on PRs to help review
    • Triaging operations for members of the core and contributor experience teams
    • A typical workflow for triaging issues
  • Maintainer Information
    • Releasing
      • Reference Steps
    • Updating Authors List
    • Guideline for bumping minimum versions of our dependencies
    • Merging Pull Requests
    • The scikit-learn.org Website
    • Experimental Features
  • Developing with the Plotting API
    • Plotting API Overview
    • Plotting with Multiple Axes
    • Using matplotlib
  • Developing with the callback API
    • Implementing callback support in estimators
      • The CallbackSupportMixin class
      • The CallbackContext class
      • The with_callbacks decorator
      • Minimal example
    • Developing callbacks
      • The callback protocol
      • Auto-propagated callbacks
      • Callback shared state
      • Minimal example

scikit-learn is financially supported by Probabl and other institutions.

Probabl

The Tabular AI company by the creators of scikit-learn
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