Csrank

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* The library has been migrated to pytorch. This is a breaking change. You will
likely need to adapt to this new version if you have been using estimators
from version 1.x.

* The RankNet and CmpNet estimators are now trained with a loss that applies to
the whole result (the general/discrete choice or ranking). They were
previously trained on object pairs with different loss functions.

* Behavior and default parameters of the estimators may differ from the
previous versions. For example the default activation for CmpNet and RankNet
is now SELU instead of ReLU.

* The dataset generators in `csrank.dataset_reader` are no longer imported
on the top level.

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* We no longer override any of the defaults of our default optimizer (SGD). In
particular, the parameters nesterov, momentum and lr are now set to the
default values set by keras.

* All optimizers must now be passed in uninitialized. Optimizer parameters can
be set by passing `optimizer__{kwarg}` parameters to the learner. This
follows the scikit-learn and skorch standard.

* Regularizers must similarly be passed uninitialized, therefore the
`reg_strength` parameter is replaced by `kernel_regularizer__l`.

* Tuning functionality has been removed. Since our Learners are ScikitLearn
estimators, any standard tuning framework should work and no special support
is needed.

* The tunable class and notably its `set_tunable_parameters` function has been
removed. Use `set_params` from the scikit-learn estimator API instead.

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* Make all our optional dependencies mandatory to work around a bug in our
optional imports code. Without this, an exception is raised on import.
A proper fix will follow.

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* Change public interface of the learners to be more in line with the
scikit-learn interface (ongoing). As part of these changes, it is no longer
required to explicitly pass the data dimensionality to the learners on
initialization.
* Rewrite and document normalized discounted cumulative gain (ndcg) metric to
fix numerical issues.
See `32 <https://github.com/kiudee/cs-ranking/issues/32>`__ for details.
* Fix passing fit keyword arguments on to the core network in
``FATEChoiceFunction``.
* Fix arguments for ``AllPositive`` baseline.
* Raise ValueError rather than silently using a default value for unknown
passed arguments.
* Internal efforts to increase code quality and make use of linting
(``black``, ``flake8``, ``doc8``).
* Remove old experimental code.

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* Add the expected reciprocal rank (ERR) metric.
* Fix bug in callbacks causing the wrong learning rate schedule to be applied.
* Make csrank easier to install by making some dependencies optional.
* Add guidelines for how to contribute to the project.

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* Fix deployment to GH-pages