# put lsh data into buckets link( slshdata, lsh.reducelsh, candataU ) sortfile( candataU, candata ) pp.mergecand( candata ) timer.send('Reduce LSH') #candstats(dataset , bic.getinfo(agdataO)) # generate biclusters link( candata, bic.genbic, bicdata, (bic.getinfo(agdataO), probabilityO, thr, (min_rows,min_cols), sparse, True), append=True ) timer.send('Gen. Bicluster') ''' timer.close() #pp.filterbics(bicdata) pp.merge(dataset) #pp.hierclust(dataset,7) pp.uncovered(dataset, bic.getinfo(agdataO)) ev.stats(dataset) # print some results statistics ev.microprecision(dataset) # calculate the microprecision if the objects have class embedded on their names ev.NMI(dataset) ev.PMI(dataset) #net.genetwork(dataset) # generate a network of objects and features #ev.stats(dataset,'LSH') # print some results statistics #ev.microprecision(dataset,'LSH') # calculate the microprecision if the objects have class embedded on their names #ev.NMI(dataset,'LSH') #ev.PMI(dataset,'LSH') if __name__ == "__main__":
pp.mergecand(candata) timer.send('Reduce LSH') #candstats(dataset , bic.getinfo(agdataO)) # generate biclusters link(candata, bic.genbic, bicdata, (bic.getinfo(agdataO), probabilityO, thr, (min_rows, min_cols), sparse, True), append=True) timer.send('Gen. Bicluster') timer.close() #pp.filterbics(bicdata) pp.merge(dataset) #pp.hierclust(dataset,7) pp.uncovered(dataset, bic.getinfo(agdataO)) ev.stats(dataset) # print some results statistics ev.microprecision( dataset ) # calculate the microprecision if the objects have class embedded on their names ev.NMI(dataset) ev.PMI(dataset) ev.stats(dataset, 'LSH') # print some results statistics ev.microprecision( dataset, 'LSH' ) # calculate the microprecision if the objects have class embedded on their names ev.NMI(dataset, 'LSH')