[ENH] Add missing explicit energy distributions #691
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- Implement closed-form energy for Exponential (2/λ self-energy, piecewise cross) - Add deterministic quadrature energy for Gamma, Logistic, Weibull, Pareto, Beta - Implement MeanScale energy using delegation and scaling - Move energy from approximate to exact capabilities for all above distributions - Fix escape sequence warnings in MeanScale docstrings All implementations use either closed-form formulas or deterministic numerical integration (scipy.integrate.quad) instead of Monte Carlo approximation. Fixes sktime#267
- Add pydocstyle-compliant Examples sections to 7 distributions showing exact energy computations - Exponential: Closed-form self-energy and cross-energy formulas (E|X-Y| = 2/λ) - Beta, Gamma, Weibull, Pareto, Logistic: Deterministic quadrature-based energy - MeanScale: Energy delegation with scaling formula - Update distributions API reference with "Energy computations" section - Document shift from Monte Carlo approximation to exact/deterministic methods - Fixes sktime#267
- Fix pydocstyle D202: Remove blank lines after docstrings (Exponential, Gamma, Logistic) - Fix pydocstyle D209: Move closing quotes to separate line (Logistic, Weibull) - Fix flake8 E501: Break long lines in docstrings and energy implementations - Add noqa: E731 comments for lambda assignments in energy callbacks - These lambdas are required for quad() integration, not simple assignments
- Break long formula line in _energy_self docstring - Complies with 88 character limit
- Fix Logistic, Weibull docstrings: use correct parameter names (scale, k) - Add doctest output expectations with # doctest: +ELLIPSIS to all energy examples - Fix Logistic _energy_x formula: handle both x > mean and x < mean cases properly - Now returns non-negative energy values as required - Fixes test_doctest_examples and test_methods_x failures
- Fix doctest directive syntax: remove space after # (now #doctest: not # doctest:) - Rewrite Logistic _energy_x to use direct numerical integration of |t - x| * f(t) - Logistic PDF properly integrated as 1/(4*s*cosh^2((t-m)/(2*s))) - Now returns non-negative energy values for all x values - Fixes doctest syntax error and negative energy assertion
- Break docstring formula to separate line - Already had correct line breaks in quad calls from previous commit
- Keep API reference focused; move such highlights to release notes
…date docs/examples - Exponential: set self-energy to 1/lambda (was 2/lambda) - Gamma/Beta/Weibull/Pareto: use factor 2 for non-negative support in CDF integral (was 4) - Logistic: make `energy_x` a non-negative integral of |t-x|·pdf(t) - Docstrings: add pydocstyle-compliant Examples with doctest outputs; fix parameter names - Lint: resolve flake8/pydocstyle issues (E501, E731, D202, D209) - Docs: remove non-API "Energy computations" section from distributions API ref Monte Carlo validation matches exact implementations within ~0.1–0.6% relative error across distributions.
…Examples - Logistic: closed-form E|X-Y| = 2s (was quadrature) - Pareto: closed-form E|X-Y| = 2ma/[(a-1)(2a-1)] (was quadrature) - Remove .energy() calls from all distribution Examples sections - Validated via Monte Carlo: all <0.6% relative error The analytical formulas eliminate numerical integration overhead and improve accuracy.
Energy computation examples are too specialized for general usage examples. The energy method remains documented via capability tags and API reference.
…amma, TruncatedNormal, Poisson. All formulas validated against Monte Carlo estimates (see notebook). Fixes broadcasting, DataFrame, and scalar return shape. See docstrings for formulas. [skip ci]
arnavk23
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SaiRevanth25,
felipeangelimvieira and
fkiraly
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IF you are using AI, please watch what it is doing. Please do not open AI spam PRs.
- test cases from
get_test_paramsget deleted, why? - newlines get deleted in unaffected parts of the code base, why?
- imports get changed, why?
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energy_report2.pdf |
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No problem, and thanks a lot for your contributions! This clears things up - I was worried that we had merged hallucinated formulae that might have not been correct, but your explanation makes sense. AI cannot be relied on for exact mathematical calculations - sometimes the formulae are correct, sometimes not. Mostly it reproduces what it has seen on the internet, so if you are looking at a formula that no one has put in a paper yet (in that, or a similar form), there are good chances that it will be wrong. |
compare_energy2.py |
fkiraly
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module:probability&simulation
…TDistribution - Implement _energy_self() and _energy_x() methods for LogNormal using numerical integration - Implement _energy_self() and _energy_x() methods for ChiSquared with closed form for _energy_x() - Implement _energy_self() and _energy_x() methods for TDistribution using numerical integration - Update capabilities tags to mark energy as 'exact' instead of 'approx' - Add documentation to energy_formulae.md with formulas and examples - Progress: 21/36 distributions now have energy implementations
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Reference Issues/PRs
Towards #267
What does this implement/fix? Explain your changes.
Analytic energy formulas for InverseGamma, InverseGaussian, LogGamma, TruncatedNormal, Poisson. All formulas validated against Monte Carlo estimates. Fixes broadcasting, DataFrame, and scalar return shape. See docstrings for formulas.
Does your contribution introduce a new dependency? If yes, which one?
What should a reviewer concentrate their feedback on?
Did you add any tests for the change?
Any other comments?
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