Sam

Changes
- Added a new abstract base class for all SAM models called `SamQuantileRegressor`, that contains some required abstract methods (fit, train, score, dump, load) any subclass needs to implement as well as some default implementations like a standard feature engineer. `SamQuantileMLP` is now a subclass of this new abstract base class, new classes will follow soon.

Changes
- `sam.visualization._evaluate_performance` now checks for nan in both `y_hat` and `y_true`.

Changes
- `sam.visualization.performance_evaluation_fixed_predict_ahead` accepts `metric` parameter that indicates what metric to evaluate the performance with: 'R2' or 'MAE' (mean absolute error). Default metric is 'R2'.

Changes

- No more bandit linting errors: replace `assert` statements
- Remove faulty try-except-pass constructions

New features
- Function `sam.utils.contains_nan` and `sam.utils.assert_contains_nan` are added for validation

Changes
- Scikit-learn version had to be <0.24.0 for certain features, TODO: update dependencies in the near future
- Updated README, setup.py and CONTRIBUTING in preparation for going open-source.

Bugfix
- `LinearQuantileRegression` only contains parameters and pvalues, and data is no longer stored in
the class. This was unwanted.

New features
- `LinearQuantileRegression` accepts `fit_intercept` parameter, similar to `sklearn.LinearRegression`.