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*** How to run NPCMF? ***

The starting point for running NPCMF is:

RUN_Cross_Validation.m
    This is for estimating the prediction performance of NPCMF using cross 
    validation. More precisely, 100 repetitions of 5-fold cross validation 
    are performed, and then the AUPRs from the five repetitions are 
    averaged to give the final AUPR.

To run, simply access the above file in MATLAB, and press F5.


In the above file, there are many options that may be set, including:

> classifier
> use_WKNKN
> cv_setting
> cross validation parameters: m, n

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*** Algorithm entry points ***

The main entry script for the  algorithm is:

> alg_NPcmf.m

Some parameters may be manually set to a fixed value, while others are 
estimated via nested CV. These parameters may be found in the above files, 
or if their values are determined by nested CV, they may be found in 
scripts that have the suffix "parest_":

> alg_NPcmf_parest.m

However, for WKNKN in particular, the parameters may be found in 
RUN_Cross_Validation.m as WKNKN may be used as a preprocessing method by 
any of the other methods.

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*** Output Description ***

In  data set, each repetition of 5-fold CV will have something similar
to the following printed on screen

==========================
n-fold experiment start:  	12 : 15 : 47.514    18-Sep-2018
****k100		2	0.125	0.25		0.693				TIME:    12 : 17 : 31.774    18-Sep-2018
****k100		2	0.125	0.25		0.715				TIME:    12 : 19 : 16.324    18-Sep-2018
****k100		2	0.125	0.25		0.717				TIME:    12 : 21 : 0.684    18-Sep-2018
****k100		2	0.125	0.125		0.632				TIME:    12 : 22 : 45.194    18-Sep-2018
****k100		2	0.125	0.25		0.731				TIME:    12 : 24 : 28.894    18-Sep-2018
n-fold experiment end:  	12 : 24 : 28.894    18-Sep-2018

      AUC: 0.942157
     AUPR: 0.697636
==========================

 FINAL AVERAGED RESULTS

     AUC (std): 0.94285	(0.0011097)
    AUPR (std): 0.699901(0.00614259)
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==========================

> The start and end times of the 5-fold CV are printed on screen.

> Each of the 5 folds takes its turn being the test set, resulting in 100 
  CV experiments. For each of these experiments, a line starting with 
  '****' holds the parameters estimated, and the last value to the right 
  (before the printed time) is the AUPR.

> After the 5-fold CV is over, the AUPRs from the 5 folds are averaged to
  give the average AUPR of the 5-fold CV. The average AUC is also given.


Finally, the AUPRs from the 100 repetitions of 5-fold CV are averaged to 
give the final AUPR of the data set.

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*** Additional notes ***

In the NPCMF file, be aware of the difference between the small 'k'
(rank of latent feature matrices A and B) and the capital 'K' (the number 
of known nearest neighbors for the WKNKN preprocessing method)

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