def test_gam_ranked_mushrooms_runtime(self):
     local_attribution_path = 'data/mushrooms.csv'
     g = GAM(attributions_path=local_attribution_path,
             n_clusters=3,
             cluster_method="ranked medoids")
     g.generate()
     print("Ranked Medoids Runtime: ", g.duration)
 def test_gam_kernel_mushrooms_runtime(self):
     local_attribution_path = '../data/mice_protein.csv'
     g = GAM(attributions_path=local_attribution_path,
             n_clusters=3,
             cluster_method="kernel medoids",
             dataset='mice_protein')
     g.generate()
     print("Kernel Runtime: ", g.duration)
 def test_gam_bandit_mushrooms_runtime(self):
     local_attribution_path = 'data/mushrooms.csv'
     g = GAM(attributions_path=local_attribution_path,
             n_clusters=3,
             cluster_method="bandit pam",
             dataset='mushrooms')
     g.generate()
     print("BanditPAM Runtime: ", g.duration)
 def test_gam_bandit_mice_runtime_700_samples(self):
     local_attribution_path = '../data/mice_protein.csv'
     g = GAM(attributions_path=local_attribution_path,
             n_clusters=3,
             cluster_method="bandit pam",
             num_samp=700,
             dataset='mice_protein')
     g.generate()
     print("BanditPAM 700 Runtime: ", g.duration)
 def test_gam_bandit_crime_runtime_1000_samples(self):
     local_attribution_path = '../data/crime.csv'
     g = GAM(attributions_path=local_attribution_path,
             n_clusters=3,
             cluster_method="bandit pam",
             num_samp=1000,
             dataset='crime')
     g.generate()
     print("BanditPAM 1000 Runtime: ", g.duration)
 def test_gam_bandit_wine_runtime(self):
     print(
         "-- test_gam_bandit_wine_runtime -------------------------------------------------------"
     )
     gam = GAM()
     local_attribution_path = 'data/wine.csv'
     g = GAM(attributions_path=local_attribution_path,
             n_clusters=3,
             cluster_method="bandit pam")
     g.generate()
     print("BanditPAM Runtime: ", g.duration, "\n")
예제 #7
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 def test_gam_parallel_wine_bestk(self):
     local_attribution_path = '../data/wine_clean.csv'
     bestClusterNumber = 0
     bestScore = -2
     for k in range(2,5):
         g = GAM(attributions_path=local_attribution_path, n_clusters=k, cluster_method="bandit pam", dataset='wine')
         g.generate()
         if g.avg_silhouette_score > bestScore:
             bestScore = g.avg_silhouette_score
             bestClusterNumber = k
     print("Best Number of Clusters: ", bestClusterNumber)
     print("Best Silhouette Score:, ", bestScore)
 def test_gam_parallel_mice_bestk(self):
     local_attribution_path = '../data/mice_protein.csv'
     bestClusterNumber = 0
     bestScore = -2
     for k in range(2, 5):
         g = GAM(attributions_path=local_attribution_path,
                 n_clusters=k,
                 cluster_method="parallel medoids",
                 dataset='mice_protein')
         g.generate()
         if g.avg_silhouette_score > bestScore:
             bestScore = g.avg_silhouette_score
             bestClusterNumber = k
     print("Best Number of Clusters: ", bestClusterNumber)
     print("Best Silhouette Score:, ", bestScore)
예제 #9
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 def test_gam_kmedoids_wine_runtime(self):
     local_attribution_path = '../data/wine_clean.csv'
     g = GAM(attributions_path=local_attribution_path, n_clusters=3, cluster_method=None)
     g.generate()
     print("Original Medoids Algorithm Runtime: ", g.duration)
예제 #10
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 def test_gam_bandit_wine_runtime(self):
     local_attribution_path = '../data/wine_clean.csv'
     g = GAM(attributions_path=local_attribution_path, n_clusters=3, cluster_method="bandit pam", dataset='wine')
     g.generate()
     print("BanditPAM Runtime: ", g.duration)
예제 #11
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 def test_gam_parallel_wine_runtime(self):
     local_attribution_path = '../data/wine_clean.csv'
     g = GAM(attributions_path=local_attribution_path, n_clusters=3, cluster_method="parallel medoids")
     g.generate()
     print("Parallel Medoids Runtime: ", g.duration)
 def test_gam_bandit_mushrooms_runtime(self):
     gam = GAM()
     local_attribution_path = 'data/mushroom-attributions-200-samples.csv'
     g = GAM(attributions_path=local_attribution_path, n_clusters=3, cluster_method="bandit pam")
     g.generate()
     print("BanditPAM Runtime: ", g.duration)