Pykoop

This release removes `pandas` from `setup.py`.

**Full changelog**: https://github.com/decargroup/pykoop/compare/v2.0.0...v2.0.1

This release introduces two breaking changes, necessitating a new major version:

1. The deprecated `KoopmanPipeline.predict_multistep()` method has been removed.
2. They `kernel_or_ift` parameter of `RandomFourierKernelApprox` has been renamed to `kernel_or_ft`, and the corresponding `ift_` attribute has been renamed to `ft_`.

Other than bug fixes, the most notable improvement is the significant reduction of import time, which is due to the removal of the `pandas` dependency.

**Full changelog**: https://github.com/decargroup/pykoop/compare/v1.2.3...v2.0.0

New features

* Removed `pandas` dependency to resolve slow imports (166)

Bug fixes

* Fixed incorrect argument names for kernel approximation (175)
* Fixed bug when using `multioutput='raw_values'` regression metric keyword argument when scoring (164)
* Fixed prediction bug when no inputs are used (173)
* Fixed `scikit-learn` method resolution order (177)
* Fixed default LMI strictness (168)

This release once again fixes a problem in the Read the Docs config file.

**Full changelog**: https://github.com/decargroup/pykoop/compare/v1.2.2...v1.2.3

This release fixes a typo in the Read the Docs config file.

**Full changelog**: https://github.com/decargroup/pykoop/compare/v1.2.1...v1.2.2

This release adds a missing Read the Docs config file that was preventing the documentation from being built.

**Full changelog**: https://github.com/decargroup/pykoop/compare/v1.2.0...v1.2.1

This release allows users to manually set a Koopman matrix computed outside of `pykoop` for easier interoperability with other libraries or custom regression code. The release also adds global configuration management, specifically to skip input validation. This can significantly (2x!) speed up `predict_trajectory()` and other methods that call `lift()`, `retract()`, etc. frequently. Finally, this release fixes some awkward scoring behaviour, making `score_trajectory()` work better in hyperparameter optimization setups.

**Full changelog**: https://github.com/decargroup/pykoop/compare/v1.1.3...v1.2.0

New features

* Added `DataRegressor` to allow `KoopmanPipeline` objects to be created directly from NumPy arrays (129)
* Added sklearn-style configuration management (`set_config(skip_validation)`, `get_config()` and `config_context(skip_validation)` ) to allow skipping input validation in performance-critical areas (151)
* Added `KoopmanPipeline.frequency_response()` to compute the frequency response of a Koopman system without plotting the Bode plot (143)
* Added the `plot_error` parameter to `plot_predicted_trajectory()` to allow plotting the prediction error instead of the trajectory (148)
* Made `DelayLiftingFn` compatible with `SplitPipeline` (145)

Bug fixes

* Fixed bug where `score_trajectory()` could return a worse score than the `error_score`, or even return `NaN` (132)