Tools
=====

Landscaping and Parameter Tuning
--------------------------------

:mod:`mlpy` includes executable scripts to be used off-the-shelf for landscaping and parameter
tuning tasks. The classification and optionally feature ranking operations are organized
in a sampling procedure (k-fold or Monte Carlo cross validation). 
       
	* :command:`svm-landscape`: landscaping and regularization parameter (*C*) tuning
	* :command:`fda-landscape`: landscaping and regularization parameter (*C*) tuning	
	* :command:`srda-landscape`: landscaping and alpha parameter (*alpha*) tuning
	* :command:`pda-landscape`: landscaping and number of regressions parameter (*Nreg*) tuning
	* :command:`dlda-landscape`
	* :command:`nn-landscape`: landscaping

Error (:func:`mlpy.err`), Matthews Correlation Coefficient (:func:`mlpy.mcc`) and 
optionally Canberra Distance (:func:`mlpy.canberra`) are retrieved at each parameter 
step.

:mod:`mlpy` includes executable scripts to be used exclusively for parameter tuning tasks:

	* :command:`irelief-sigma`: kernel width parameter (*sigma*) tuning

In order to print help message:

.. code-block:: bash
   
   $ command --help


Other Tools
-----------

:command:`borda`
	
Compute Borda Count, Extraction Indicator, Mean Position Indicator from a text file
containing feature lists.


:command:`canberra`
	
Compute mean Canberra distance indicator on top-k sublists from a text file
containing feature lists and one contanining the top-k positions.


In order to print help message:

.. code-block:: bash
   
   $ command --help


The Feature Lists File
^^^^^^^^^^^^^^^^^^^^^^
The feature lists file is a plain text TAB-separated file where each row is a feature ranking
(a feature list).

Example::

   feat6 [TAB] feat2 [TAB] ... [TAB] feat1
   feat4 [TAB] feat1 [TAB] ... [TAB] feat7
   feat4 [TAB] feat9 [TAB] ... [TAB] feat3
   feat2 [TAB] feat3 [TAB] ... [TAB] feat9
   feat8 [TAB] feat4 [TAB] ... [TAB] feat2