Package: mlnods
mlNODSsplit machine learning data sets using graph partioningAppropriate assessments require appropriate splits of training and evaluation data sets and this in turn requires clustering. For many problems single-linkage clustering suffices toward this end. Encountering a problem that could not be solved by such a standard procedure, we developed a simple graph-based tool for the creation of unique data sets.mlNODS is a graph-based method which allows to split original data sets into non-overlapping sets that cannot be grouped without removing some of the data. mlNODS optimizes the following constraints: (1) retain as many data points as possible, and (2) remove any overlap between two split sets. The nodes of our graph are the original data points, and the connections are measures for the similarity between nodes (e.g. sequence similarity for protein sets). The method begins by building the full graph and proceeds by removing nodes in order to optimally fit the constraints to the similarity table. mlNODS is applicable to any prob- lem and has the additional benefit of allowing overlap within one set (i.e. training on homologues) while it is disal- lowed between two sets (i.e. training and testing do not overlap).usage:mlnods[-h]-sSPLITS-cCUTOFF[-lLIMIT]-eEDGES_FILE[-fEDGES_FORMAT]-nNODES_FILE[-a][-rRANDOM][-oOUTFOLDER][-v][-q][--version]Thisisascriptthatwillcreateindependentsetsofdata

Version:1.0[03/14/20]optionalarguments:-h,--helpshowthishelpmessageandexit-sSPLITS,--splitsSPLITSnumberofsplitsrequired-cCUTOFF,--cutoffCUTOFFsimilaritycutoffintheunitsoflinkscores-lLIMIT,--limitLIMITlimitonthenumberoflinksforeachnode(default=0,infinity)-eEDGES_FILE,--edgesEDGES_FILEfilecontainingatableofinstanceswithlinkscoresforeachpair-fEDGES_FORMAT,--formatEDGES_FORMATformatofthetablefileblast:takesalistof-m9formatedblastfilesandbuildsatablebasedonseqIDhssp:takesalistof-m9formatedblastfiles,runsHSSPscoringscriptandbuildsanHSSPdistancetableself<int>:space/tabseparatedtablefile,similarityscoreincolumn<int>eg"ID1 ID2 similarity_score"willbeaddressedasself3(default=self5)-nNODES_FILE,--nodesNODES_FILEinstancefilecontainingIDsofallinstancesbeingconsideredIDsarecase-independent(egABC=abc)IDsarealwayspreceededby">"andfollowedbyawhitespace.NowhitespacesareallowedinanID.IfscoreisprovidedforanID,itshouldbesurroundedbyspacesanddirectlyfollowtheID(eg.>abl1_human10geneassociatedwith....)EverythingbetweentwoIDsisprintedinthejunctionfiles,butnotconsideredinevaluation-a,--abundancetheoptiontoscorefalse:scoreretrievedfrominstancefile,range[0-100],default=50whenmissingtrue:scoreapproximatedbyactualnumberoftimesanIDappearsintheinstancefile-rRANDOM,--randomRANDOMsetafixedrandomseedtogenerateconsistentpartitions-oOUTFOLDER,--outfolderOUTFOLDERpathtooutputfolder(default=<currentdirectory>-v,--verbosesetverbositylevel-q,--quietnologgingtostdout--versionshowprogram'sversionnumberandexitIfanIDispresentintheinstancefile,butnotinthetablefiletheIDisconsideredtonotbelinkedtoanythingelseIfanIDispresentinthetablefilebutnotintheinstancefile,itisignored

mlnodswasdevelopedbyYanaBrombergandrefactoredbyMaximilianMiller.

Feelfreetocontactusforsupportatservices@bromberglab.org.
Thissoftwareislicensedunder[NPOSL-3.0](http://opensource.org/licenses/NPOSL-3.0)