XGBoost gap-filling
[XGBoost](https://xgboost.readthedocs.io/en/stable/index.html) can now be used to fill gaps in time series data.
In `diive`, `XGBoost` is implemented in class `XGBoostTS`, which adds additional options for easily including e.g.
lagged variants of feature variables, timestamp info (DOY, month, ...) and a continuous record number. It also allows
direct feature reduction by including a purely random feature (consisting of completely random numbers) and calculating
the 'permutation importance'. All features where the permutation importance is lower than for the random feature can
then be removed from the dataset, i.e., the list of features, before building the final model.
`XGBoostTS` and `RandomForestTS` both use the same base class `MlRegressorGapFillingBase`. This base class will also
facilitate the implementation of other gap-filling algorithms in the future.
Another fun (for me) addition is the new class `TimeSince`. It allows to calculate the time since the last occurrence of
specific conditions. One example where this class can be useful is the calculation of 'time since last precipitation',
expressed as number of records, which can be helpful in identifying dry conditions. More examples: 'time since freezing
conditions' based on air temperature; 'time since management' based on management info, e.g. fertilization events.
Please see the notebook for some illustrative examples.
**Please note that `diive` is still under developement and bugs can be expected.**
New features
- Added gap-filling class `XGBoostTS` for time series data,
using [XGBoost](https://xgboost.readthedocs.io/en/stable/index.html) (`diive.pkgs.gapfilling.xgboost_ts.XGBoostTS`)
- Added new class `TimeSince`: counts number of records (inceremental number / counter) since the last time a time
series was inside a specified range, useful for e.g. counting the time since last precipitation, since last freezing
temperature, etc. (`diive.pkgs.createvar.timesince.TimeSince`)
Additions
- Added base class for machine learning regressors, which is basically the code shared between the different
methods. At the moment used by `RandomForestTS` and `XGBoostTS`. (`diive.core.ml.common.MlRegressorGapFillingBase`)
- Added option to change line color directly in `TimeSeries` plots (`diive.core.plotting.timeseries.TimeSeries.plot`)
Notebooks
- Added new notebook for gap-filling using `XGBoostTS` with mininmal settings (`notebooks/GapFilling/XGBoostGapFillingMinimal.ipynb`)
- Added new notebook for gap-filling using `XGBoostTS` with more extensive settings (`notebooks/GapFilling/XGBoostGapFillingExtensive.ipynb`)
- Added new notebook for creating `TimeSince` variables (`notebooks/CalculateVariable/TimeSince.ipynb`)
Tests
- Added test case for XGBoost gap-filling (`tests.test_gapfilling.TestGapFilling.test_gapfilling_xgboost`)
- Updated test case for random forest gap-filling (`tests.test_gapfilling.TestGapFilling.test_gapfilling_randomforest`)
- Harmonized test case for XGBoostTS with test case of RandomForestTS
- Added test case for `TimeSince` variable creation (`tests.test_createvar.TestCreateVar.test_timesince`)