Introducing better customisation

[ppdebreuck]

This PR combines various changes to enable better user customisation of the MODNet pipeline. The workflow is high-level by design. Nevertheless, it might be useful to add custom parameters without breaking the current code.

This includes:

• Running feature selection only on a subset of properties present in the MODData. feature_ selection() now enables this with ignore_names.
• By default, FitGenetic will proceed by using joint-learning when multiple targets are given in the MODData. This can now be avoided by using ignore_names in FitGenetic().
• MODNetModel.fit() can take optional fit_params that are passed through to Keras model.fit().
• fit_params can also be passed to FitGenetic.run()
• MODNetModel.fit() can take a custom loss function.
• FitGenetic() can take a custom loss function.
• Custom data can be passed trough MODNetModel.fit(). It will be appended to the targets (axis=-1). This can be useful for defining custom loss functions.
• Any property called custom_data in FitGenetic is ignored, and appended to the targets (axis=-1). This can be useful for defining custom loss functions.

[ml-evs]

lgtm