.. _aggregate: 

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Fast-aggregate
==============

The fast-aggregate program aggregates trained ACP and VAP models, it does not work for precomputed data or TCP. 
It will merge multiple models into a large aggregated model, but 
it does no sanitation at all. Use it for speeding up training of larger ACP and VAP models, especially when using LibSvm
which has a much longer training time for a given training set size.  


.. contents:: Table of Contents
   :depth: 3
   :backlinks: top


Parameters
----------
The full usage menu can be retrieved by running command: 

.. code-block:: text

   > java -jar cpsign-[version].jar fast-aggregate
   
                                         fast-aggregate
   SYNOPSIS
   ------------------------------------------------------------------------------------------
     fast-aggregate [options]
     fast-aggregate @/tmp/runconfigs/parameters.txt [options]
     fast-aggregate @C:\Users\User\runconfigs\parameters.txt [options]
   
   
   DESCRIPTION
   ------------------------------------------------------------------------------------------
     The fast-aggregate program joins partially trained predictors into the final aggregated
     one. This program facilitate a high level way of distributing the training step for ACP
     and VAP predictors. Use the --splits flag in the 'train' program to train a partial
     predictor, and then use fast-aggregate to merge everything together.
   
   
   OPTIONS
   ------------------------------------------------------------------------------------------
     Input:
     * -m  | --modelfiles                       [[URI | path] [URI | path] ..]
          A list (space or comma-separated) of models that should be aggregated. It is
          allowed to give directories, glob patterns (with wildcard characters), explicit
          files or URIs. Note that models can be a mix of non-encrypted and encrypted models.
       -af | --accept-fail
          Accept failure if a model cannot be added to the aggregated model (i.e. if model is
          of wrong type etc.). Default is to fail execution
   
     Output:
     * -mo | --model-out                        [path]
          Model file to generate
   
     General:
     * --license                                [URI | path]
          Path or URI to license file
       -h  | --help | man
          Get help text
       --short
          Use shorter help text (used together with the --help argument)
       --logfile                                [path]
          Path to a user-set logfile, will be specific for this run
       --silent
          Silent mode (only print output to logfile)
       --echo
          Echo the input arguments given to CPSign
       --seed                                   [integer]
          Set this flag if an explicit RNG seed should be used in tasks that require a RNG
          (randomization of training data, splitting in cross-validation, learning algorithms
          etc). Not used by all programs.
       --progress-bar
          Add a Progress bar in the system error output
       --progress-bar-ascii
          Add a Progress bar in ASCII in the system error output
       --time
          Print wall-time for all individual steps in execution
   
   ------------------------------------------------------------------------------------------



Example usage
-------------

.. code-block:: bash
   
   > java -jar cpsign-[version].jar fast-aggregate \
      --license /path/to/Standard-license.license \
      -m \
      models/acp_model_reg_1.cpsign \
      models/acp_model_reg_2.cpsign \
      --model-out \
      /tmp/aggregated_reg.cpsign \
      
   Running with Standard License registered to [Name] at [Company]. Expiry
   date is [Date]
   
   Starting to aggregate models..
   Successfully aggregated 2 models.
   
   Aggregated model saved at:
   /private/tmp/aggregated_reg.cpsign