.. _fwflag_transform_to_feats:
=======================
--transform_to_feats
=======================
Switch
======

--transform_to_feats NAME

Description
===========

Transforms a feature table through a fitted dimensionality-reduction model (from :doc:`fwflag_fit_reducer`) and writes the reduced components into a new feature table.

Argument and Default Value
==========================

A name used to build the destination table (``feat$dr_<model>_<name>$<corptable>$<correl_field>``). Default: ``None``.

Details
=======

Verified end-to-end: with ``--fit_reducer --model pca --n_components 5 --transform_to_feats pca_reduced``, created ``feat$dr_pca_pca_reduced$msgs$user_id`` and inserted one feature row per group for each of ``COMPONENT_0`` through ``COMPONENT_4``, printing a row count per component as it went.

Other Switches
==============

Required Switches:

* :doc:`fwflag_fit_reducer`
* :doc:`fwflag_model`

Optional Switches:

* :doc:`fwflag_n_components`

Example Commands
================

Using the packaged ``dla_tutorial`` dataset (see :doc:`../../tutorials/tut_clustering`):

.. code-block:: bash

	dlatkInterface.py -d dla_tutorial -t msgs -c user_id -f 'feat$1to3gram$msgs$user_id$0_05' \
	--fit_reducer --model pca --n_components 5 --transform_to_feats pca_reduced