data_path = '/content/'
file_prefix = 'yoochoose_clicks_sampled'
buys_prefix = 'yoochoose_buys_sampled'
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))
data_path = 'data/{}/single/'.format(database_used)
data_trained = 'data/{}/prepared2d/'.format(database_used)
file_prefix = file_prefixes[database_used]
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) )