Model-diagnostics

Highlights
- Isotonic regression for all expectiles and quantiles enable reliability diagrams for those functionals and the score decomposition for all scoring functions that are consistent for them, e.g. for the pinball loss.
- New example on quantile regression
- The function `decompose` can now deal with multiple predictions at once, similar to `compute_bias`.

What's Changed
* DOC exclude test directories in gen_reg_pages.py by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/68
* MNT update to polars 0.17.2 by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/73
* MNT update mkdocs-material 9.1 and mkdocs-jupyter 0.24 by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/70
* ENH add generalized PAVA for isotonic regression by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/74
* ENH add IsotonicRegression class by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/78
* ENH add quantile to reliability diagram by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/79
* ENH add quantiles and expectile to scoring.decompose by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/81
* MNT update gitignore excluding some ipynb by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/82
* MNT update dependencies by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/83
* CI add monthly run of test matrix by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/84
* MNT deploy docs on release instread of on push by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/85
* MNT increase mypy to version 1.4 by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/86
* ENH add confidence_level to plot_bias by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/87
* ENH support multiple y_pred models in scoring.decompose by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/88
* DOC add quantile regression example by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/91
* DOC extend readme and index by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/93
* REL increase to version 1.0.0rc0 by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/92
* DOC update index, readme and quantile regression example by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/94
* REL increase to version 1.0.0 by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/95


**Full Changelog**: https://github.com/lorentzenchr/model-diagnostics/compare/v0.2.0...v1.0.0

What's Changed
* DOC exclude test directories in gen_reg_pages.py by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/68
* MNT update to polars 0.17.2 by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/73
* MNT update mkdocs-material 9.1 and mkdocs-jupyter 0.24 by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/70
* ENH add generalized PAVA for isotonic regression by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/74
* ENH add IsotonicRegression class by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/78
* ENH add quantile to reliability diagram by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/79
* ENH add quantiles and expectile to scoring.decompose by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/81
* MNT update gitignore excluding some ipynb by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/82
* MNT update dependencies by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/83
* CI add monthly run of test matrix by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/84
* MNT deploy docs on release instread of on push by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/85
* MNT increase mypy to version 1.4 by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/86
* ENH add confidence_level to plot_bias by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/87
* ENH support multiple y_pred models in scoring.decompose by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/88
* DOC add quantile regression example by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/91
* DOC extend readme and index by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/93
* REL increase to version 1.0.0rc0 by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/92


**Full Changelog**: https://github.com/lorentzenchr/model-diagnostics/compare/v0.2.0...v1.0.0rc0

What's Changed
* DOC hyperlink in highlights of frontpage by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/46
* CI codecov setup by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/47
* DOC add codecov badge by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/48
* DOC add link to release notes by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/49
* MNT add .codecov.yml by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/50
* TST add test_compute_bias_1d_array_like by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/51
* CI upload coverage report only once by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/52
* FIX p-value when stderr is zero but bias_count>=2 by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/54
* ENH keep null values in compute_bias for low n_bins by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/53
* DOC add trunk-based development by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/55
* DOC fix typos by mayer79 in https://github.com/lorentzenchr/model-diagnostics/pull/56
* FIX account for Null value when binning in compute_bias by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/57
* FIX fix logic for string features with n_bins in compute_bias by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/58
* ENH plot null values in plot_bias by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/59
* ENH add ElementaryScore by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/60
* FIX ElementaryScore always non-negative by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/62
* ENH add plot_murphy_diagram by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/63
* ENH plot_bias with Null values by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/65
* DOC add Murphy plot to regression example by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/66
* REL increase to version 0.2.0 by lorentzenchr in https://github.com/lorentzenchr/model-diagnostics/pull/67

New Contributors
* mayer79 made their first contribution in https://github.com/lorentzenchr/model-diagnostics/pull/56

**Full Changelog**: https://github.com/lorentzenchr/model-diagnostics/compare/v0.1.1...v0.2.0

Enhancements:
- Support NaN and Null in `compute_bias` (PR 43)

Bug Fixes:
- Always output column `"bias_weights"` in `compute_bias` (PR 44)

Some highlights:
- Confidence intervals for `plot_reliability_diagram` via arguments `n_bootstrap` and `confidence_level` (PR 32).
- New option `diagram_type = "bias"` for `plot_reliability_diagram`, which is roughly a 45 degree rotated plot (PR 35).
- Better visualisation of uncertainty/standard errors in `plot_bias` and distinction between numerical and categorical features (PR 37).
- Consistently sorted output, i.e. the different (model) columns of `y_pred` (PR 37).
- Number of effective (output) bins is now always at most `n_bins` in `compute_bias` and `plot_bias` (PR 37).
- Switch to [ruff](https://beta.ruff.rs/) (PR #34)

A new module `scoring` containing:
- Add strictly consistent, homogeneous scoring functions
- `HomogeneousExpectileScore` for mean an expectiles
- `HomogeneousQuantileScore` for quantiles
- `SquaredError`, `PoissonDeviance`, `GammaDeviance` and `PinballLoss` for convenience
- Add `LogLoss`
- Add score decomposition `decompose` 🚀
To my knowledge, this is the first time the score decomposition into miscalibration, discrimination (or resolution) is available in Python. R users can use the wonderful [reliabilitydiag package](https://cran.r-project.org/package=reliabilitydiag) of aijordan for quite some time now.