[MRG]  Refactoring and expanding sklearn.preprocessing scaling

Hi there!

This PR refactors the data-scaling code from  `sklearn.preprocessing` to remove some duplicated code, and adds some new features. More specifically:

* adds `RobustScaler` and `robust_scale` functions that use robust estimates of data center/scale (median & interquartile range), which should work better for outliers.

* Adds possibility to scale by sample instead of by feature, via an `axis=1` parameter (the `scale` function could already do this, now the *Scaler classes can, too!)

* adds `minmax_scale` function (requrested by @ogrisel)

* adds `MaxAbsScaler`, similar in functionality to `MinMaxScaler`, but also works on Sparse Matrices, as proposed in the discussions of  #1799.

* Reuses the code common to `StandardScaler`, `RobustScaler`, `MinMaxScaler` and `MaxAbsScaler`. by  putting it in an abstract base class. Essentially the `*Scaler` classes are only responsible for estimating the necessary statistics in `fit`, the rest of the `Transformer` API (transform/inverse_transform, handling sparseness/different axis-parameters) is implemented in a `BaseScaler`, as this code is common to all of the scalers.
 This caused some parameters to be renamed and some attributes to be renamed: `with_centering` and `with_scaling` are the parameters that control if scaling/centering is performed and the `center_` and `scale_` attributes are used to store the centering/scaling values.

* `*_scale` functions now simply reuse the `*Scaler` classes internally to avoid code duplication.

* adds a lot of new tests for all the new functionality.


**Notes and Caveats**
* ~~StandardScaler had parameters `with_mean` and `with_std` which are renamed to `with_centering` and `with_scaling` to fall in line with the other Scalers. I wasn't sure how to handle deprecating the old parameter names  in `__init__` -- what's the protocol here?~~

* `RobustScaler` cannot be fitted on sparse matrices:
    - centering doesn't make sense because it risks destroying sparsity (similar to what StandardScaler does)
    - Scaling doesn't work  because there is no decent code to calculate the IQR of sparse matrices available in scipy
   ~~As an alternative, we could advice people to scale using the MinMaxScaler instead to scale features to the same range with::~~

 >>> scaler = MinMaxScaler()
 >>> scaler.with_centering=False
  >>> scaler.fit_transform(X)

~~(`MinMaxScaler` doesn't support the `with_centering` parameter directly because I wasn't sure if this would lead to confusion).~~
Is there any reason to believe that one interpolation strategy is better than another for robust scaling?