Bugfix and Brush Up On Benchmark

README.md

@@ -8,7 +8,7 @@ general approach taken is to take the standard HDBSCAN algorithm and swap compon
 non-Euclidean data for scalable primitives. This often requires resorting to algorithms that are approximations of what
 is done in HDBSCAN, sometimes without approximation guarantees.
 
-Note: This code works best if the input data is de-duplicated, with exact duplicate points removed. 
+Note: This code works best if the input data is de-duplicated, with exact duplicate points removed.
 If not, HDBSCAN will identify clusters with duplicate points as highly stable and always select them.
 
 
@@ -84,7 +84,7 @@ source ./venv/bin/activate
 # In clams/build
 # -m: min cluster size
 # -s: min samples
-python3 ../script/benchmark/hdbscan/run_hdbscan.py -m 10 -s 5 -p ../dataset/fashion-mnist/points.txt -g ../dataset/fashion-mnist/labels.txt
+python3 ../script/benchmark/hdbscan_benchmark/run_hdbscan.py -m 10 -s 5 -p ../dataset/fashion-mnist/points.txt -g ../dataset/fashion-mnist/labels.txt
 ```
 
 # License

script/benchmark/clustering_utilities.py

@@ -30,10 +30,14 @@ def eval_clusters(cluster_labels, true_labels, singleton_cluster_to_noise_points
             print(f"After filtering: {len(true_labels)} ground truth points")
 
     non_noise_points_mask = (cluster_labels >= 0)
-    pct_clustered = (np.sum(non_noise_points_mask) / cluster_labels.shape[0])
-    print(f"Cluster coverage (%): {pct_clustered * 100:.2f}")
+    num_non_noise_points = np.sum(non_noise_points_mask)
+    pct_clustered = (num_non_noise_points / cluster_labels.shape[0])
+    print(f"Cluster coverage (%): {pct_clustered * 100:.2f}%")
 
-    if len(non_noise_points_mask) < cluster_labels.shape[0]:  # Has noise points
+    # Make sure always num_non_noise_points <= cluster_labels.shape[0]
+    assert num_non_noise_points <= cluster_labels.shape[0]
+
+    if num_non_noise_points != cluster_labels.shape[0]:  # Has noise points
         if singleton_cluster_to_noise_points:
             print(
                 "Assigning a singleton cluster to each noise point in the clustering result")
@@ -99,7 +103,7 @@ def find_files_in_dir(dir_path, ext=''):
 # If the first column is not a point ID, set has_ids to False.
 # If there are multiple files in a directory, the point IDs must be present.
 def read_point_data(data_path, has_ids=True):
-    print(f"Loading data from {data_path}")
+    print(f"Loading data from {data_path}", flush=True)
     files = find_files_in_dir(data_path)
 
     if len(files) == 0:
@@ -137,7 +141,7 @@ def read_point_data(data_path, has_ids=True):
 
             points_table[pid] = list(map(float, items))
 
-    print(f"Loaded {len(points_table)} items from {len(files)} files")
+    print(f"Loaded {len(points_table)} items from {len(files)} files", flush=True)
 
     # numpy array of feature vectors
     # if the IDs are not continuous, fill the missing IDs with -1
@@ -167,7 +171,7 @@ def read_point_data(data_path, has_ids=True):
 #
 # Both File types can also contain comment lines, which must start from #.
 def read_label_data(data_path):
-    print(f"Loading data from {data_path}")
+    print(f"Loading data from {data_path}", flush=True)
     labels_dict = {}
     files = []
     if os.path.isdir(data_path):
@@ -186,9 +190,9 @@ def read_label_data(data_path):
             break
 
     if contains_ids:
-        print("Loading point IDs and labels")
+        print("Loading point IDs and labels", flush=True)
     else:
-        print("Loading only labels")
+        print("Loading only labels", flush=True)
 
     if len(files) > 1 and not contains_ids:
         print("Multiple files are provided,"
@@ -220,8 +224,8 @@ def read_label_data(data_path):
                 exit(1)
             labels_dict[pid] = label
 
-    print(f"Loaded {len(labels_dict)} items from {len(files)} files")
-    print(f"Max ID: {max_id}")
+    print(f"Loaded {len(labels_dict)} items from {len(files)} files", flush=True)
+    print(f"Max ID: {max_id}", flush=True)
 
     # numpy array of labels
     # if the IDs are not continuous, fill the missing IDs with -1

script/benchmark/extract_clams_clustering_time.sh

@@ -18,7 +18,7 @@ if [[ $# -eq 0 ]]; then
   exit 1
 fi
 
-printf "kNNG k,nodes,tasks/node,kNNG (s),MFC (s),AMST (s),CLAMS-HDBSCAN (s),Assigning noise points (s),file\n"
+printf "kNNG k,nodes,tasks/node,kNNG (s),MFC (s),AMST (s),CLAMS-HDBSCAN (s),Noise clustering (s),file\n"
 
 for file in "$@"; do
   awk -v fname="$file" '