| Name | model-diagnostics JSON |
| Version |
1.3.0
JSON |
| download |
| home_page | None |
| Summary | Tools for diagnostics and assessment of (machine learning) models |
| upload_time | 2024-11-27 20:54:15 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | MIT License
Copyright (c) 2022 Christian Lorentzen
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE. |
| keywords |
calibration
machine learning
model diagnostics
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# model-diagnostics
| | |
| --- | --- |
| CI/CD |[](https://github.com/lorentzenchr/model-diagnostics/actions/workflows/test.yml) [](https://codecov.io/gh/lorentzenchr/model-diagnostics)
| Docs | [](https://github.com/lorentzenchr/model-diagnostics/actions/workflows/docs.yml)
| Package | [](https://pypi.org/project/model-diagnostics/) [](https://pypi.org/project/model-diagnostics/) [](https://pypi.org/project/model-diagnostics/) |
| Meta | [](https://github.com/pypa/hatch) [](https://github.com/astral-sh/ruff) [](https://github.com/python/mypy) [](https://spdx.org/licenses/)
**Tools for diagnostics and assessment of (machine learning) models**
Highlights:
- All common point predictions covered: mean, median, quantiles, expectiles.
- Assess model calibration with [identification functions](https://lorentzenchr.github.io/model-diagnostics/reference/model_diagnostics/calibration/identification/#model_diagnostics.calibration.identification.identification_function) (generalized residuals), [compute_bias](https://lorentzenchr.github.io/model-diagnostics/reference/model_diagnostics/calibration/identification/#model_diagnostics.calibration.identification.compute_bias) and [compute_marginal](https://lorentzenchr.github.io/model-diagnostics/reference/model_diagnostics/calibration/identification/#model_diagnostics.calibration.identification.compute_marginal).
- Assess calibration and bias graphically
- [reliability diagrams](https://lorentzenchr.github.io/model-diagnostics/reference/model_diagnostics/calibration/plots/#model_diagnostics.calibration.plots.plot_reliability_diagram) for auto-calibration
- [bias plots](https://lorentzenchr.github.io/model-diagnostics/reference/model_diagnostics/calibration/plots/#model_diagnostics.calibration.plots.plot_bias) for conditional calibration
- [marginal plots](https://lorentzenchr.github.io/model-diagnostics/reference/model_diagnostics/calibration/plots/#model_diagnostics.calibration.plots.plot_marginal) for average `y_obs`, `y_pred` and partial dependence for one feature
- Assess the predictive performance of models
- strictly consistent, homogeneous [scoring functions](https://lorentzenchr.github.io/model-diagnostics/reference/model_diagnostics/scoring/scoring/)
- [score decomposition](https://lorentzenchr.github.io/model-diagnostics/reference/model_diagnostics/scoring/scoring/#model_diagnostics.scoring.scoring.decompose) into miscalibration, discrimination and uncertainty
- Choose your plot backend, either [matplotlib](https://matplotlib.org) or [plotly](https://plotly.com/python/), e.g., via [set_config](https://lorentzenchr.github.io/model-diagnostics/reference/model_diagnostics/#model_diagnostics.set_config).
:rocket: To our knowledge, this is the first python package to offer reliability diagrams for quantiles and expectiles and a score decomposition, both made available by an internal implementation of isotonic quantile/expectile regression. :rocket:
Read more in the [documentation](https://lorentzenchr.github.io/model-diagnostics/).
This package relies on the giant shoulders of, among others, [polars](https://pola.rs/), [matplotlib](https://matplotlib.org), [scipy](https://scipy.org) and [scikit-learn](https://scikit-learn.org).
**Installation**
```
pip install model-diagnostics
```
**Contributions**
Contributions are warmly welcome!
When contributing, you agree that your contributions will be subject to the [MIT License](https://github.com/lorentzenchr/model-diagnostics/blob/main/LICENSE).
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