Sam

Changes
- Make `'ID'`and `'TYPE'` columns `pd.Categorical` instead of `str`, to reduce the memory spike when using `pd.pivot_table` in `sam_format_to_wide`.
- Added parameter in `QuantileRegressor` to use HiGHS solver, as recommended in <https://docs.scipy.org/doc/scipy/reference/optimize.linprog-highs.html>. This will also keep the package compatible with future versions of SciPy.

Changes
- Allow numpy versions up to 1.23.x. 1.24 is not yet supported by shap (and shap does not specify this constraint in its requirements). For future reference, note that numpy 1.24 is also not supported by h5py versions below 3.0.0 (again without specifying) as it uses the deprecated `np.typeDict`. h5py is a requirement of tensorflow.
- Upgrade tensorflow
- Limit scikit-learn version <2

Fixes
- `ConstantTimeseriesRegressor` now fills nan values in input data with zero before calling `preprocess_fit` in order to successfully (by)pass validation from `BaseTimeseriesRegressor`. Besides scikit-learn compatibility, the input data is not actually used when fitting.

Changes
- `ConstantTimeseriesRegressor` no longer checks dtypes of input data, nor nan/inf values, as the input is only used to determine the shape of the predictions.

Changes
- Updated `BaseTimeseriesRegressor.get_feature_names_out()` so, in case of the feature engineer is a `Pipeline`, it returns the names from the last `ColumnTransformer`, if available

Changes
- Updated wrong types in `quantile_plot.py`