Brush Up On Benchmark

script/benchmark/bench_utilities.py

@@ -1,44 +1,10 @@
 # Copyright 2023-2026 Lawrence Livermore National Security, LLC and other ClaMS
 # Project Developers. See the top-level COPYRIGHT file for details.
 
-
 import subprocess
 import os
 
-
-# Execute a shell command
-# out_file: output file to write the stdout
-# err_file: output file to write the stderr
-# command: command to execute
-# cwd: working directory
-def execute_cmd(log_file, err_log_file, command, cwd='./'):
-    print(f'In {os.path.abspath(cwd)}')
-    print(f'Command: {command}')
-    result = subprocess.run(f'{command}', shell=True,
-                            cwd=cwd,
-                            stdout=subprocess.PIPE,
-                            stderr=subprocess.PIPE)
-
-    with open(log_file, 'a') as f:
-        f.write(f'Command: {command}')
-        f.write(result.stdout.decode("utf8"))
-    with open(err_log_file, 'a') as f:
-        f.write(result.stderr.decode("utf8"))
-
-    if result.returncode == 0:
-        print("Command executed successfully.\n")
-    else:
-        print(f"Error executing command. See {err_log_file}.\n")
-        exit(1)
-
-
-def create_dir(dir_path):
-    os.makedirs(dir_path, exist_ok=True)
-    # Check if the directory was created
-    if not os.path.exists(dir_path):
-        print(f"Error: Could not create directory {dir_path}")
-        exit(1)
-
+from script.utilities import *
 
 def set_up_batch_header(script_file, job_name, job_dir, num_nodes):
     # Make dir
@@ -76,31 +42,3 @@ def add_srun_cmd(num_tasks_per_node, command, script_file, echo=True,
                  check_return_code=True):
     add_cmd(f"srun --ntasks-per-node={num_tasks_per_node} {command}",
             script_file, echo, check_return_code)
-
-
-def grep_file(file, text_pattern):
-    # Return the lines that contain the pattern
-    with open(file, 'r') as f:
-        lines = f.readlines()
-        return [line for line in lines if text_pattern in line]
-
-# Parse a range string and return a list of integers
-# e.g., "1-5" -> [1, 2, 3, 4, 5]
-# e.g., "1,3,5" -> [1, 3, 5]
-def parse_range(range_str):
-    if range_str is None:
-        return [None]
-
-    if '-' in range_str:
-        start, end = range_str.split('-')
-        return list(range(int(start), int(end) + 1))
-    else:
-        return [int(x) for x in range_str.split(',')]
-
-# Parse a list of floats from a string and return a list of floats
-# e.g., "1.0,2.5,3.14" -> [1.0, 2.5, 3.14]
-def parse_float_list(float_list_str):
-    if float_list_str is None:
-        return [None]
-
-    return [float(x) for x in float_list_str.split(',')]

script/benchmark/clustering_utilities.py

@@ -6,7 +6,8 @@
 from sklearn.metrics import adjusted_rand_score, adjusted_mutual_info_score
 import datetime
 import os
-
+import sys
+from script.utilities import *
 
 # Modified from:
 # https://gist.github.com/lmcinnes/24ed5c22c80125be5133811d677eae7b
@@ -75,162 +76,3 @@ def assign_singleton_cluster_to_noise_points(cluster_labels, noise_id_offset):
     print(f"Assigned singleton clusters to {cnt_noise} noise points")
 
     return new_labels
-
-
-def show_time_now():
-    now = datetime.datetime.now()
-    print(now.strftime("%Y-%m-%d %H:%M:%S"))
-
-
-def find_files_in_dir(dir_path, ext=''):
-    if not os.path.isdir(dir_path):
-        return [dir_path]
-
-    files = []
-    for file in os.listdir(dir_path):
-        if len(ext) == 0 or file.endswith(ext):
-            files.append(os.path.join(dir_path, file))
-    return files
-
-
-# Read point(feature) data from file(s)
-# If data_path is a directory, load all files in the directory.
-# Return a dense numpy array of feature vectors.
-# Each row is a feature vector.
-# Input files can have point IDs in the first column.
-# Point IDs are expected to be 0, 1, 2, ..., N-1,
-# where N is the number of points.
-# 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}", flush=True)
-    files = find_files_in_dir(data_path)
-
-    if len(files) == 0:
-        print(f"No files found in {data_path}")
-        exit(1)
-
-    if len(files) > 1 and not has_ids:
-        print("Multiple files are provided,"
-              "but has_ids is false.")
-
-    points_table = {}
-    dimensions = 0
-    for file in files:
-        for line in open(file, 'r'):
-            if len(line.strip()) == 0:
-                print(f"Empty line found in {file}")
-                continue
-            items = line.split()
-            if has_ids:
-                pid = int(items[0])
-                items = items[1:]
-            else:
-                pid = len(points_table)
-
-            if pid in points_table:
-                print(f"Duplicate ID in the feature file: {pid}")
-                exit(1)
-
-            if dimensions == 0:
-                dimensions = len(items)
-            elif dimensions != len(items):
-                print(
-                    f"Dimension mismatch in {file}: {len(items)} != {dimensions}")
-                exit(1)
-
-            points_table[pid] = list(map(float, items))
-
-    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
-    max_id = max(points_table.keys())
-    points = np.full((max_id + 1, len(points_table[0])), 0.0)
-    for id, feature in points_table.items():
-        points[id] = feature
-
-    return points
-
-
-# Load cluster label or ground truth label data
-# If data_path is a directory, load all files in the directory
-# Return a dense numpy array of labels
-# Unclustered points are filled with -1
-#
-# There are two accepted file types:
-#
-# 1. Only cluster IDs:
-# Each line contains a cluster ID.
-# Point IDs are assumed to be 0, 1, 2, ..., N-1, where N is the number of points.
-# This mode is not acceptable if there are multiple files in a directory.
-#
-# 2. Point IDs and cluster IDs:
-# Contains two columns:
-# the first column is for point IDs and the second column is for cluster IDs.
-#
-# 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}", flush=True)
-    labels_dict = {}
-    files = []
-    if os.path.isdir(data_path):
-        files = [os.path.join(data_path, f) for f in os.listdir(data_path)]
-    else:
-        files.append(data_path)
-
-    if len(files) == 0:
-        print(f"No files found in {data_path}")
-        exit(1)
-
-    contains_ids = False
-    for line in open(files[0], 'r'):
-        if len(line.split()) >= 2:
-            contains_ids = True
-            break
-
-    if contains_ids:
-        print("Loading point IDs and labels", flush=True)
-    else:
-        print("Loading only labels", flush=True)
-
-    if len(files) > 1 and not contains_ids:
-        print("Multiple files are provided,"
-              "but no IDs are found in the first file.")
-
-    max_id = 0
-    for file in files:
-        for line in open(file, 'r'):
-
-            # Skip comments
-            if line.startswith("#"):
-                continue
-
-            if contains_ids:
-                items = line.split()
-                if len(items) < 2:
-                    print(f"Invalid line: {line}")
-                    exit(1)
-                pid = int(items[0])
-                label = int(items[1])
-                max_id = max(max_id, pid)
-            else:
-                pid = len(labels_dict)
-                max_id = pid
-                label = int(line)
-
-            if pid in labels_dict:
-                print(f"Duplicate ID in the ground truth file: {pid}")
-                exit(1)
-            labels_dict[pid] = label
-
-    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
-    labels = np.full(max_id + 1, -1)
-    for id, label in labels_dict.items():
-        labels[id] = label
-
-    return labels

script/benchmark/evaluate_clustering_quality.py

@@ -37,7 +37,7 @@
 import argparse
 import numpy as np
 from clustering_utilities import *
-
+from script.utilities import *
 
 def parse_options():
     parser = argparse.ArgumentParser(
@@ -63,8 +63,8 @@ def parse_options():
 def main():
     opt = parse_options()
 
-    cluster_labels = read_label_data(opt.cluster_path)
-    true_labels = read_label_data(opt.gt_path)
+    cluster_labels = read_cluster_labels(opt.cluster_path, "*")
+    true_labels = read_cluster_labels(opt.gt_path, "*")
 
     try:
         eval_clusters(cluster_labels, true_labels, opt.singleton_noise_points, opt.ignore_true_noise_points)