Sysidentpy

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CONTRIBUTORS
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- wilsonrljr
- dj-gauthier
- mtsousa

CHANGES
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- **New Features:**
- **MAJOR**: Ridge Regression Parameter Estimation:
- Introducing Ridge algorithm for model parameter estimation. Set `estimator="ridge_regression"` and control regularization with the `alpha` parameter. Special thanks to dj-gauthier and mtsousa for their contribution. Users are encouraged to visit https://www.researchgate.net/publication/380429918_Controlling_chaos_using_edge_computing_hardware to explore how dj-gauthier used SysIdentPy in his research.

- **API Changes:**
- Improved `plotting.py` code with type hints and new options for plotting results.
- Refactored methods to resolve future warnings from numpy.
- Code refactoring following PEP8 guidelines.
- Set "default" as the default style for plotting to avoid errors in new versions of matplotlib.

- **Datasets:**
- Added `buck_id.csv` and `buck_valid.csv` datasets to the SysIdentPy repository.

- **Documentation:**
- Add NFIR example. The notebook show how to build models without past output regressors (using only input regressors).
- Enhanced usage example for MetaMSS.
- Continued adding type hints to methods.
- Improved docstrings throughout the codebase.
- Minor additions and grammar fixes in documentation.
- dj-gauthier provided valuable suggestions for enhancing the documentation, which are currently undergoing refinement and will soon be accessible.

- **Development Tools:**
- Added pre-commit hooks to the repository.
- Enhanced `pyproject.toml` to assist contributors in setting up their own environment.

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CONTRIBUTORS
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- wilsonrljr
- gamcorn
- Gabo-Tor

CHANGES
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- The update **v0.3.3** has been released with additional features, API changes and fixes.

- MAJOR: Multiobjective Framework: Affine Information Least Squares Algorithm (AILS)
- Now you can use AILS to estimate parameters of NARMAX models (and variants) using a multiobjective approach.
- AILS can be accessed using `from sysidentpy.multiobjective_parameter_estimation import AILS`
- See the docs for a more in depth explanation of how to use AILS.
- This feature is related to Issue 101. This work is the result of an undergraduate research conducted by Gabriel Bueno Leandro under the supervision of Samir Milani Martins and Wilson Rocha Lacerda Junior.

- API Change: `regressor_code` variable was renamed as `encoding` to avoid using the same name as the method in `narmax_tool` `regressor_code` method.

- DATASET: Added buck_id.csv and buck_valid.csv dataset to SysIdentPy repository.

- DOC: Add a Multiobjective Parameter Optimization Notebook showing how to use the new AILS method

- DOC: Minor additions and grammar fixes.

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CONTRIBUTORS
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- wilsonrljr

CHANGES
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- The update **v0.3.2** has been released with API changes and fixes.

- Major:
- Added Akaike Information Criteria corrected in FROLS. Now the user can use aicc as the information criteria to select the model order when using FROLS algorithm.

- FIX: Issue 114. Replace yhat with y in root relative squared error. Thanks miroder

- TESTS: Minor changes in tests by removing unnecessary data load.

- Remove unused code and comments.

- Docs: Minor changes in notebooks. Added AICc method in the information criteria example.

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CONTRIBUTORS
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- wilsonrljr

CHANGES
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- The update **v0.3.1** has been released with API changes and fixes.

- API Change:
- MetaMSS was returning the max lag of the final model instead of the maximum lag related to the xlag and ylag. This is not wrong (its related to the issue 55), but this change will be made for all methods at the same time. In this respect, I'm reverted this to return the maximum lag of the xlag and ylag.

- API Change: Added build_matrix method in BaseMSS. This change improved overall code readability by rewriting if/elif/else clauses in every model structure selection algorithm.

- API Change: Added bic, aic, fpe, and lilc methods in FROLS. Now the method is selected by using a predefined dictionary with the available options. This change improved overall code readability by rewriting if/elif/else clauses in the FROLS algorithm.

- TESTS: Added tests for Neural NARX class. The issue with pytorch was fixed and now we have the tests for every model class.

- Remove unused code and comments.

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CONTRIBUTORS
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- wilsonrljr
- gamcorn
- Gabo-Tor

CHANGES
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- The update **v0.3.0** has been released with additional features, API changes and fixes.

- MAJOR: Estimators support in AOLS
- Now you can use any SysIdentPy estimator in AOLS model structure selection.

- API Change:
- Refactored base class for model structure selection. A refactored base class for model structure selection has been introduced in SysIdentPy. This update aims to enhance the system identification process by preparing the package for new features that are currently in development, like multiobjective parameter estimation, new basis functions and more.

Several methods within the base class have undergone significant restructuring to improve their functionality and optimize their performance. This reorganization will facilitate the incorporation of advanced model selection techniques in the future, which will enable users to obtain dynamic models with robust dynamic and static performance.
- Avoid unnecessary inheritance in every MSS method and improve the readability with better structured classes.
- Rewritten methods to avoid code duplication.
- Improve overall code readability by rewriting if/elif/else clauses.

- Breaking Change: `X_train` and `y_train` were replaced respectively by `X` and `y` in `fit` method in MetaMSS model structure selection algorithm. `X_test` and `y_test` were replaced by `X` and `y` in `predict` method in MetaMSS.

- API Change: Added BaseBasisFunction class, an abstract base class for implementing basis functions.

- Enhancement: Added support for python 3.11.

- Future Deprecation Warning: The user will have to define the estimator and pass it to every model structure selection algorithm instead of using a string to define the Estimator. Currently the estimator is defined like "estimator='least_squares'". In version 0.4.0 the definition will be like "estimator=LeastSquares()"

- FIX: Issue 96. Fix issue with numpy 1.24.* version. Thanks for the contribution gamcorn.

- FIX: Issue 91. Fix r2_score metric issue with 2 dimensional arrays.

- FIX: Issue 90.

- FIX: Issue 88 .Fix one step ahead prediction error in SimulateNARMAX class (thanks for pointing out, Lalith).

- FIX: Fix error in selecting the correct regressors in AOLS.

- Fix: Fix n step ahead prediction method not returning all values of the defined steps-ahead value when passing only the initial condition.

- FIX: Fix Visible Deprecation Warning raised in get_max_lag method.

- FIX: Fix deprecation warning in Extended Least Squares Example

- DATASET: Added air passengers dataset to SysIdentPy repository.

- DATASET: Added San Francisco Hospital Load dataset to SysIdentPy repository.

- DATASET: Added San Francisco PV GHI dataset to SysIdentPy repository.

- DOC: Improved documentation in Setting Specif Lags page. Now we bring an example of how to set specific lags for MISO models.

- DOC: Minor additions and grammar fixes.

- DOC: Improve image visualization using mkdocs-glightbox.

- Update dev packages versions

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CONTRIBUTORS
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- wilsonrljr

CHANGES
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- The update **v0.2.1** has been released with additional feature, minor API changes and fixes.

- MAJOR: Neural NARX now support CUDA
- Now the user can build Neural NARX models with CUDA support. Just add `device='cuda'` to use the GPU benefits.
- Updated docs to show how to use the new feature.


- MAJOR: New documentation website
- The documentation is now entirely based on Markdown (no rst anymore).
- We use MkDocs and Material for MkDocs theme now.
- Dark theme option.
- The Contribute page have more details to help those who wants to contribute with SysIdentPy.
- New sections (e.g., Blog, Sponsors, etc.)
- Many improvements under the hood.

- MAJOR: Github Sponsor
- Now you can support SysIdentPy by becoming a Sponsor! Details: https://github.com/sponsors/wilsonrljr

- Tests:
- Now there are test for almost every function.
- Neural NARX tests are raising numpy issues. It'll be fixed til next update.

- FIX: NFIR models in General Estimators
- Fix support for NFIR models using sklearn estimators.

- The setup is now handled by the pyproject.toml file.

- Remove unused code.

- Fix docstring variables.

- Fix code format issues.

- Fix minor grammatical and spelling mistakes.

- Fix issues related to html on Jupyter notebooks examples on documentation.

- Updated Readme.