[ENH] fit_compute_func API

[alegonz]

• Add new fit_compute_func to Step.__call__ that allows to specify custom behavior at fit time.
• Add example of stacked classifiers that follow the standard protocol.
• Use "auto" to specify automatic detection of compute_func and fit_compute_func, reserve None to disable the fit_compute_func.
• Add capability of specifying extra attributes to classes created with make_step via a attr_dict argument. This is useful to add custom fit_predict/fit_transform methods.

The motivation follows the rationale in Issue #16:

1. Make custom fitting protocols, such as the common stacking protocol that uses out-of-fold predictions in the first level, possible. (The current stacked classifier example is a naive example that does not use OOF predictions and thus the second level classifier is prone to prefer an overfitted classifier from the first level).
2. Allow the use of transductive estimators (e.g. sklearn.manifold.TSNE, sklearn.cluster.AgglomerativeClustering).
3. Leverage estimators that implement a fit_transform more efficient than calling fit and transform separately.

[codecov-io]

Codecov Report

Merging #22 into master will increase coverage by 0.03%.
The diff coverage is 98.76%.

@@            Coverage Diff            @@
##           master     #22      +/-   ##
=========================================
+ Coverage   97.87%   97.9%   +0.03%     
=========================================
  Files          13      13              
  Lines         705     764      +59     
  Branches      130     142      +12     
=========================================
+ Hits          690     748      +58     
  Misses         11      11              
- Partials        4       5       +1

Impacted Files	Coverage Δ
baikal/steps/expression.py	100% <ø> (ø)	⬆️
baikal/_core/step.py	100% <100%> (ø)	⬆️
baikal/steps/factory.py	100% <100%> (ø)	⬆️
baikal/_core/model.py	99.59% <96.29%> (-0.41%)	⬇️

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