limit_train = None #limit in number of rows or None limit_test = None #limit in number of rows or None density_value = 1 #randomly filter out events (0.0-1.0, 1:keep all) remove_imdups = False export_csv = 'results/test-results.csv' print(data_path) # create a list of metric classes to be evaluated metric = [] metric.append(ac.HitRate(20)) metric.append(ac.HitRate(10)) metric.append(ac.HitRate(5)) metric.append(ac.HitRate(3)) metric.append(ac.MRR(20)) metric.append(ac.MRR(10)) metric.append(ac.MRR(5)) metric.append(ac.MRR(3)) metric.append(cov.Coverage(20)) metric.append(cov.Coverage(10)) metric.append(cov.Coverage(5)) metric.append(cov.Coverage(3)) metric.append(pop.Popularity(20)) metric.append(pop.Popularity(10)) metric.append(pop.Popularity(5)) metric.append(pop.Popularity(3)) # metric.append( div.ArtistDiversity(20) ) # metric.append( coh.ArtistCoherence(20) ) # create a dict of (textual algorithm description => class) to be evaluated
limit_train = None # Limit in number of rows or None limit_test = None # Limit in number of rows or None density_value = 1 # Randomly filter out events (0.0-1.0, 1:keep all) remove_imdups = False # sampling = "all" export_csv = 'results/results.csv'.format(database_used) # export_csv = 'results/full/results-{}-knn-context.csv'.format(database_used) # create a list of metric classes to be evaluated metric = [] metric.append(ac.HitRate(20)) # metric.append( ac.HitRate(10) ) # metric.append( ac.HitRate(5) ) # metric.append( ac.HitRate(3) ) metric.append(ac.MRR(20)) # metric.append( ac.MRR(10) ) # metric.append( ac.MRR(5) ) # metric.append( ac.MRR(3) ) metric.append(cov.Coverage(20)) # metric.append( cov.Coverage(10) ) # metric.append( cov.Coverage(5) ) # metric.append( cov.Coverage(3) ) # metric.append( pop.Popularity(20) ) # metric.append( pop.Popularity(10) ) # metric.append( pop.Popularity(5) ) # metric.append( pop.Popularity(3) ) # metric.append( div.ArtistDiversity(20) ) # metric.append( coh.ArtistCoherence(20) ) # create a dict of (textual algorithm description => class) to be evaluated