Skip to content
(backup fork since google code is going down)
C++ MATLAB R Python Shell Makefile Batchfile
Branch: master
Clone or download
Pull request Compare This branch is even with ryanbressler:master.
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Type Name Latest commit message Commit time
Failed to load latest commit information.
man Updated benchmark script. Feb 11, 2013
matlab Changed SuperFastHash to MurMurHash3. Dec 17, 2012
src Small tweaks Mar 23, 2013
test Made some dramatic changes: removed support for ARFF; made TreeData a… Mar 21, 2013
DESCRIPTION Added information about dependencies (Rcpp) Oct 2, 2012
Makefile Made some dramatic changes: removed support for ARFF; made TreeData a… Mar 21, 2013
NAMESPACE Added information about dependencies (Rcpp) Oct 2, 2012
doxy.cfg Patching over changes from a localized Git copy of the project. Unfor… Aug 11, 2011
make_win32.bat Fixed a bug in hasher that was preventing the code to run properly un… Jan 11, 2013
test_103by300_mixed_matrix.afm Revised the test feature matrix. May 3, 2012
test_2by8_numerical_matrix.tsv Added a new split method for the Splitter class: it looks up the spli… Jan 4, 2012
test_3by10_categorical_matrix.tsv Shrinkage parameter is now updated properly when RF is grown. Updated… Feb 20, 2012
test_predictor.sf Update to support creating ternary splits, where the third branch con… Dec 28, 2012
test_rfacer.R Added tester script for the R package Oct 7, 2012


#summary Manual pages.

*The manual pages have been written on the basis of RF-ACE verson 0.5.5*

= Description =

RF-ACE is an efficient C++ implementation of a robust machine learning algorithm for uncovering multivariate associations from large and diverse data sets. RF-ACE natively handles numerical and categorical data with missing values, and potentially large quantities of noninformative features are handled gracefully utilizing artificial contrast features, bootstrapping, and p-value estimation.

= Installation =

Download the latest stable release from the [ download page], or checkout the latest development version (to directory rf-ace/) by typing
svn checkout rf-ace

Compiler makefiles for Linux (`Makefile`) and Visual Studio for Windows (`make.bat`) are provided in the package. In Linux, you can compile the program by typing 
make rf_ace

In Windows and using Visual Studio, first open up the Visual Studio terminal and execute `make.bat` by typing
Simple as that! If you feel lucky, check for compiled binaries at the [ download page]. 

= Supported data formats =
RF-ACE currently supports two file formats, Annotated Feature Matrix (AFM) and Attribute-Relation File Format (ARFF).

== Annotated Feature Matrix (AFM) ==

Annotated Feature Matrix represents the data as a tab-delimited table, where both columns and rows contain headers describing the samples and features. Based on the headers, the AFM reader is able to discern the right orientation (features as rows or columns in the matrix) of the matrix. Namely AFM feature headers must encode whether the feature is (`N`)umerical, (`C`)ategorical, (`O`)rdinal, or (`B`)inary, followed by colon and the actual name of the feature as follows:

 * `B:is_alive`
 * `N:age`
 * `C:tumor_grage` 
 * `O:anatomic_organ_subdivision`

In fact any string, even including colons, spaces, and other special characters, encodes a valid feature name as long as it starts with the preamble `N:`/`C:`/`O:`/`B:`. Thus, the following is a valid feature header:

 * `N:GEXP:TP53:chr17:123:456`

Sample headers are not constrained, except that they must not contain preambles `N:`/`C:`/`O:`/`B:`, being reserved for the feature headers. 

== Attribute-Relation File Format (ARFF) ==

[ ARFF specification].      

= Usage =
The following examples follow Linux syntax. Type 
bin/rf_ace --help
bin/rf_ace -h
to bring up help:
 -I / --input        input feature file (AFM or ARFF)
 -i / --target       target, specified as integer or string that is to be matched with the content of input
 -O / --output       output association file

 -n / --ntrees       number of trees per RF (default nsamples/nrealsamples)
 -m / --mtry         number of randomly drawn features per node split (default sqrt(nfeatures))
 -s / --nodesize     minimum number of train samples per node, affects tree depth (default max{5,nsamples/20})
 -p / --nperms       number of Random Forests (default 50)
 -t / --pthreshold   p-value threshold below which associations are listed (default 0.1)
 -g / --gbt          Enable (1 == YES) Gradient Boosting Trees, a subsequent filtering procedure (default 0 == NO)

So all that is required is an input file (`-I/--input`), either of type `.arff` or `.afm`, and a target (`-i/--target`) to build the RF-ACE model upon. Target in this case corresponds to a feature in the input file, and it can be identified with an index corresponding to it's order of appearance in the file, or with it's name. Thus, if the target is `N:age` (we would be looking for features associated with age) existing on row `123` (0-base and omitting the header row), one execute RF-ACE by typing
bin/rf_ace --input featurematrix.afm --target 123 --output associations.tsv 
or with the short-hand notation equivalently as
bin/rf_ace -I featurematrix.afm -i 123 -O associations.tsv 
or by using the header "N:age" instead of the index by typing
bin/rf_ace -I featurematrix.afm -i N:age -O associations.tsv
In case a provided (sub)string identifies multiple target candidates, RF-ACE will be executed serially for all target candidates, results catenated in the specified output file.

The above will execute RF-ACE with the default parameters; as the help documentation points out, most of the parameters are estimated dynamically based on the data dimensions and content, so running RF-ACE with no information about the algorithm itself is possible.

= Output = 
The following call (assuming now the substring `age` uniquely identifies just one feature, `N:age`)
bin/rf_ace -I featurematrix.afm -i age -O associations.tsv
produces the output

| RF-ACE -- efficient feature selection with heterogeneous data |
|                                                               |
|  Version:      RF-ACE v0.5.5, July 4th, 2011                  |
|  Project page:                |
|  Contact:                          |
|                              |
|                                                               |
|              DEVELOPMENT VERSION, BUGS EXIST!                 |

Reading file 'featurematrix.afm'
File type is unknown -- defaulting to Annotated Feature Matrix (AFM)
AFM orientation: features as rows

RF-ACE parameter configuration:
  --input      = featurematrix.afm
  --nsamples   = 223 / 282 (20.922% missing)
  --nfeatures  = 48912
  --targetidx  = 123, header 'N:age'
  --ntrees     = 356
  --mtry       = 221
  --nodesize   = 12
  --nperms     = 50
  --pthresold  = 0.1
  --output     = associations.tsv

Growing 50 Random Forests (RFs), please wait...
  RF 1: 4880 nodes (avg. 13.7079 nodes / tree)
  RF 2: 4810 nodes (avg. 13.5112 nodes / tree)
  RF 3: 4856 nodes (avg. 13.6404 nodes / tree)
  RF 4: 4994 nodes (avg. 14.0281 nodes / tree)
  RF 5: 5036 nodes (avg. 14.1461 nodes / tree)
  RF 6: 5016 nodes (avg. 14.0899 nodes / tree)
  RF 7: 5132 nodes (avg. 14.4157 nodes / tree)
  RF 47: 4736 nodes (avg. 13.3034 nodes / tree)
  RF 48: 5234 nodes (avg. 14.7022 nodes / tree)
  RF 49: 4582 nodes (avg. 12.8708 nodes / tree)
  RF 50: 5210 nodes (avg. 14.6348 nodes / tree)
50 RFs, 17800 trees, and 247516 nodes generated in 102.91 seconds (2405.17 nodes per second)
Gradient Boosting Trees *DISABLED*

Association file created. Format:


If there are no associations found, the program would end as follows:
No significant associations found, quitting...

= RF-ACE configuration =

Information will be added in the future
You can’t perform that action at this time.