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bench_stub_nx.py
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bench_stub_nx.py
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#!/usr/bin/env python3
"""
Author : Xinyuan Chen <[email protected]>
Date : 2022-08-03
Purpose: EasyGraph & NetworkX side-by-side benchmarking
"""
from hr_tddschn import hr
from pathlib import Path
from tempfile import mkstemp
import sqlite3
from functools import partial
from utils_db import insert_bench_results
from config import (
eg_master_dir,
load_functions_name,
di_load_functions_name,
clustering_methods,
shortest_path_methods,
# connected_components_methods,
connected_components_methods_G,
connected_components_methods_G_node,
mst_methods,
other_methods,
new_methods,
method_groups,
dataset_names,
BENCH_CSV_DIR,
tool_name_mapping_for_DTForTools,
bench_results_db_path,
tool_name_mapping,
)
from utils import eg2nx, eg2ceg, nx2eg, get_first_node, eval_method, json2csv, tabulate_csv
from eg_bench_types import DTForTools
# if eg_master_dir.exists():
# import sys
# sys.path.insert(0, str(eg_master_dir))
import easygraph as eg
import networkx as nx
from dataset_loaders_sampled import load_stub_nx
load_func_name = 'load_stub_nx'
original_load_func_uses_networkx = hasattr(load_stub_nx, 'load_func_for') and load_stub_nx.load_func_for == 'nx' # type: ignore
sampled_graph = hasattr(load_stub_nx, 'sampled') and load_stub_nx.sampled # type: ignore
if original_load_func_uses_networkx or sampled_graph:
G_nx = load_stub_nx()
G_eg = nx2eg(G_nx) # type: ignore
else:
G_eg = load_stub_nx()
G_nx = eg2nx(G_eg)
G_ceg = eg2ceg(G_eg)
first_node_eg = get_first_node(G_eg)
first_node_nx = get_first_node(G_nx)
first_node_ceg = get_first_node(G_ceg)
import argparse
def get_args():
"""Get command-line arguments"""
parser = argparse.ArgumentParser(
description='EasyGraph & NetworkX side-by-side benchmarking',
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
# parser.add_argument(
# '-d',
# '--dataset',
# type=str,
# choices=dataset_names,
# nargs='+',
# )
parser.add_argument(
'-G', '--method-group', type=str, choices=method_groups, nargs='+'
)
parser.add_argument(
'-E', '--skip-easygraph', action='store_true', help='Skip benchmarking easygraph (python) method',
)
parser.add_argument(
'-C',
'--skip-cpp-easygraph',
'--skip-ceg',
action='store_true',
help='Skip benchmarking cpp_easygraph methods',
)
parser.add_argument(
'-N', '--skip-networkx', action='store_true', help='Skip benchmarking networkx method',
)
# parser.add_argument('-n', '--dry-run', action='store_true', help='Dry run')
parser.add_argument(
'-p', '--pass', type=int, help='Number of passes to run in the benchmark, uses Timer.autorange() if not set.'
)
# parser.add_argument(
# '-t', '--timeout', type=int, help='Timeout for benchmarking one method in seconds, 0 for no timeout', default=60
# )
parser.add_argument(
'--paper', action='store_true', help='Use this flag to generate the results for the paper'
)
parser.add_argument(
'-o', '--output-dir', type=Path, help='Output directory', default=BENCH_CSV_DIR,
)
parser.add_argument(
'-a', '--append-results', action='store_true', help='Append results to existing csv files. Overwrites by default.'
)
parser.add_argument(
'-S', '--no-save', action='store_true', help='Do not save results to csv files or the database.'
)
parser.add_argument(
'--graph-type', type=str, choices=['directed', 'undirected', 'all'], help='Only run bench if graph is of specified graph type', default='all',
)
parser.add_argument(
'--db-path',
metavar='PATH',
type=Path,
help='Path to the sqlite3 database',
default=bench_results_db_path,
)
parser.add_argument(
'--no-update-db', action='store_true', help='Do not update the sqlite3 database with the new results.'
)
return parser.parse_args()
def main():
args = get_args()
method_groups = args.method_group
flags = {}
flags |= {'skip_eg': args.skip_easygraph}
flags |= {'skip_ceg': args.skip_cpp_easygraph}
flags |= {'skip_networkx': args.skip_networkx}
flags |= {'skip_draw': True}
flags |= {'timeit_number': getattr(args, 'pass', None)}
# flags |= {'timeout': args.timeout if args.timeout > 0 else None}
result_dicts: list[dict] = []
bench_timestamps: list[DTForTools] = []
first_node_args = {
'call_method_args_eg': ['first_node_eg'],
'call_method_args_nx': ['first_node_nx'],
'call_method_args_ceg': ['first_node_ceg'],
}
if method_groups is None or 'clustering' in method_groups or args.paper:
# bench: clustering
for method_name in clustering_methods:
_, __ = eval_method(
load_func_name,
method_name,
**flags,
)
result_dicts.append(_)
bench_timestamps.append(__)
if method_groups is None or 'shortest-path' in method_groups or args.paper:
# bench: shortest path
# bench_shortest_path(cost_dict, g, load_func_name)
_, __ = eval_method(
load_func_name,
('Dijkstra', 'single_source_dijkstra_path'),
**first_node_args,
**flags,
)
result_dicts.append(_)
bench_timestamps.append(__)
if method_groups is None or 'connected-components' in method_groups or args.paper:
# bench: connected components
for method_name in connected_components_methods_G:
_, __ = eval_method(
load_func_name,
method_name,
**flags,
)
result_dicts.append(_)
bench_timestamps.append(__)
for method_name in connected_components_methods_G_node:
_, __ = eval_method(
load_func_name,
method_name,
**first_node_args,
**flags,
)
result_dicts.append(_)
bench_timestamps.append(__)
if method_groups is None or 'mst' in method_groups or args.paper:
# bench: mst
for method_name in mst_methods:
_, __ = eval_method(
load_func_name,
method_name,
**flags,
)
result_dicts.append(_)
bench_timestamps.append(__)
if not args.paper and (method_groups is None or 'other' in method_groups):
# bench: other
for method_name in other_methods:
_, __ = eval_method(
load_func_name,
method_name,
**flags,
)
result_dicts.append(_)
bench_timestamps.append(__)
if not args.paper and (method_groups is None or 'new' in method_groups):
# bench: other
for method_name in new_methods:
_, __ = eval_method(
load_func_name,
method_name,
**flags,
)
result_dicts.append(_)
bench_timestamps.append(__)
print()
from mergedeep import merge
result = merge(*result_dicts)
# print(f'{result_dicts=}')
# print(f'{result=}')
dataset_name = load_func_name.removeprefix("load_")
csv_file = f'{dataset_name}.csv'
csv_file_path = args.output_dir / csv_file
if args.no_save:
_, csv_file_path_s = mkstemp(suffix='.csv')
csv_file_path = Path(csv_file_path_s)
args.output_dir.mkdir(parents=True, exist_ok=True)
csv_file_path_s = str(csv_file_path)
json2csv(result, csv_file_path_s, append=args.append_results)
print(f'Result saved to {csv_file_path_s} .')
# print csv_file with tabulate
print(tabulate_csv(csv_file_path_s))
if args.no_save:
csv_file_path.unlink()
print(f'Removed temporary csv file at {csv_file_path_s} .')
if args.no_update_db:
return
with sqlite3.connect(args.db_path) as conn:
print(f'Writing new results to database at {args.db_path} .')
for i, (dataset_name, data) in enumerate(result.items()):
dt_for_tools = bench_timestamps[i]
# result is like
# {'stub': {'average_clustering': {'easygraph': 0.00047430999984499067,
# 'eg w/ C++ binding': 7.46910081943497e-05,
# 'networkx': 0.00028450800164137036},
# 'clustering': {'easygraph': 0.00010412100527901202,
# 'eg w/ C++ binding': 4.4621992856264114e-05,
# 'networkx': 0.00013218499952927232}}}
for method, tool_time_mapping in data.items():
for tool, avg_time in tool_time_mapping.items():
insert_bench_results(
conn,
dataset=dataset_name,
method=method,
tool=tool_name_mapping[tool] if tool in tool_name_mapping else tool,
average_time=avg_time,
timestamp=getattr(dt_for_tools, tool_name_mapping_for_DTForTools[tool]),
iteration_count=getattr(args, 'pass', None),
)
print(f'Finished writing new results to database at {args.db_path} .')
if __name__ == "__main__":
main()