Module Dataset
==============

::

    Dataset(name, data_path=None, target_path=None, data=Empty DataFrame, 
            target=Empty DataFrame, scaler=MinMaxScaler(feature_range=(0.0, 1.0)))


After choosing two separate pandas DataFrame objects - one for the data and one
for the target labels - preprocessing steps will be taken to format the dataset.
Data will be normalized, by default with Min-Max Normalization.

Two formats are accepted: either pandas DataFrames (``data``, ``target``), or the 
path to a csv file (``data_path``, ``target_path``).

Arguments
----------

name: string.
  Name of the dataset.
  
data_path: string, default=None.
  Path to a .csv file containing the input data matrix with a header and an index.
  If None, the module will look for the input data in the ``data`` parameter.
  
target_path: string, default=None.
  Path to a .csv file containing the true labels with a header and an index.
  The file can have either one or two columns: 
    * One column with the label for each sample, each label being represented by an integer.
    * That same column, as well as a column with the label represented in a string format (the name of the class).
    
  If None, the module will look for the dataset in the ``target`` parameter.
  
data:
  pandas DataFrame object containing the input data matrix with a header and an index.
  If ``data_path`` was given, ``target`` will be overridden. 

target:
  pandas DataFrame object containing the input data matrix with a header and an index.
  If ``data_path`` was given, ``target`` will be overridden. 

scaler, default=MinMaxScaler().
  Scaler object from ``sklearn`` used to normalize the data.

Attributes
-------------
n_classes: 
  Number of classes.
  
original_data:
  Data before normalization.
  
data:
  Data after normalization, ready to be split into a training and a test set and be used
  to build the neural network.
  
target:
  Labels, represented as integers.
  
target_names:
  Dictionary associating each class to its name.