Scalecast

Added
- added the following functions that can each add additional Xvars to forecast with:
- `add_exp_terms()` - for non polynomial exponential transformations
- `add_logged_terms()` - for log of any base transformations
- `add_pt_terms()` - for individual variable power transformations (box cox and yeo johnson available)
- `add_diffed_terms()` - to difference non-y terms
- `add_lagged_terms()` - to lag non-y terms
- added the 'pt' normalizer for yeo-johnson normalization (in addition to 'minmax', 'normalize', and 'scale')
- added the `drop_Xvars()` function that is identical to the `drop_regressors()` function
Changed
- imports all sklearn models as soon as scalecast is imported
- src code cleanup with better coding practices when it comes to forecasting sklearn models (no more copying and pasting new functions)
- changed several set data types to lists in src code
- changed the names of some hidden functions
- other src code cleanup for readability and minor efficiency gains
- better in-line comments and docstring documentation
- got rid of quiet paramater in `save_summary_stats()` and `save_feature_importance()` and now these simply log any problems as warnings
- time trends now start at 1 instead of 0 (makes log transformations possible)
- observation dropping for AR terms in sklearn models now based on the number of N/A values in each AR term instead of just the AR number
- changed some example grids to include the pt normalizer
Fixed
- now logs all warnings

Added
- `dynamic_testing` argument to `manual_forecast()` and `auto_forecast()` functions -- this is `True` by default (makes all testing comparable between sklearn/non-sklearn models)
- `dynamic_tuning` argument to `tune()` function -- this is `False` by default to majorly improve speed in some applications
Changed
- native Forecaster warnings will be logged
Fixed

Added
- added `tune_test_forecast()` function to notebook module to create a progress bar when using a notebook
Changed
Fixed
- fixed an issue with `Forecaster.ingest_Xvars_df()` when `use_future_dates=False` causing an error to be raised

Added
- added `include_traing` parameter to `notebook.results_vis()` function
Changed
Fixed
- fixed `print_attr` parameter default in `notebook.results_vis()`

Added
- added `results_vis()` notebook function (requires ipywidgets)
- added `Forecaster.export_Xvars_df()` function
- added `max_integration` argument to the `Forecaster.integrate()` function
Changed
Fixed

Added
Changed
- Now reloads Grids file each time `ingest_grid()` is called so that notebooks do not have to be rerun when a grid cannot be found
Fixed
- Fixed an issue with some sklearn estimators that occurs when passing a subset of regressors in a list to the forecast function