Sam

New functions
- New class: `sam.feature_engineering.SPEITransformer` for computing precipitation and evaporation features

Bugfixes
- Fixed failing unit tests by removing tensorflow v1 code
- Fixed QuantileMLP, where the target would stay an integer, which fails with our custom loss functions
- Updated optional dependencies to everything we use
- With the latest pandas version a UTC to string conversion has been fixed. Removed our fix, upped the pandas version
- Updated scikit-learn to at least 0.21, which is required for the iterative imputer

Development changes
- Added `run-linting.yml` to run pycodestyle in devops pipelines
- Added `run-unittest.yml` to run pytest in devops pipelines
- Removed `.arcconfig` (old arcanist unit test configuration)
- Removed `.arclint` (old arcanist lint configuration)

New functions
- `sam.visualisation.sam_quantile_plot`: Options to set `outlier_window` and `outlier_limit`, to only plot anomalies when at least `outlier_limit` anomalies are counted within the `outlier window`

Bugfixes
- Bugfix in `sam.metrics.custom_callbacks`

Bugfixes
- `sam.models.SamQuantileMLP.score`: if using y_scaler, now scales actual and prediction to equalize score to keras loss

New functions
- `sam.models.SamQuantileMLP.quantile_feature_importances`: now has argument sum_time_components that summarizes feature importances for different features generated for a single component (i.e. in onehot encoding).

Changes
- `sam.feature_engineering.automatic_rolling_engineering`: `estimator_type` argument can now also be 'bayeslin', which should be used if one hot components are used

Bugfixes
- `sam.feature_engineering.automatic_rolling_engineering`: constant features are no longer deleted (broke one hot features)

Bugfixes
- `sam.models.SamQuantileMLP`: When using y_scaler, name of rescaled y-series is set correctly.

Changes
- `sam.models.SamQuantileMLP`: Now accepts a keyword argument `r2_callback_report` to add the new custom r2 callback.

New functions
- `sam.metrics.custom_callbacks`: Added a custom callback that computes r2 with `sam.metrics.train_mean_r2` for each epoch