143 lines
4.6 KiB
Python
143 lines
4.6 KiB
Python
import time
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import pandas as pd
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import numpy as np
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from tqdm import tqdm
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import datetime
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import json
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import os
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from experiments.synthetic_data.graph_generators.outdegree_graph_generator import outdegree_generate_graph
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from core.dijkstra.heap_dijkstra import heap_dijkstra
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from core.heaps.binary_heap import BinHeap
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from core.heaps.fibonacci_heap import FiboHeap
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def run_test(
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nodes_li,
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densities,
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stds,
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trials,
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mean_dist,
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start,
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base_seed
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):
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timer = time.perf_counter
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file_exists = False
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INF = float('inf')
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total_jobs = (
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len(nodes_li) *
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len(densities) *
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len(stds) *
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trials
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)
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with tqdm(total=total_jobs) as pbar:
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for nodes in nodes_li:
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rows = []
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for density in densities:
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for std in stds:
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sigma = np.sqrt(np.log((std / mean_dist)**2 + 1))
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mu = np.log(mean_dist) - (sigma**2) / 2
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distribution = ('lognormal', (mu, sigma))
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for trial in range(1, trials + 1):
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seed = (base_seed * 1_000_003) ^ (nodes * 9176) ^ int(density * 1e9) ^ int(sigma * 1e6) ^ trial
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seed &= 0xFFFFFFFF
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adj = outdegree_generate_graph(
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NODES=nodes,
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DENSITY=density,
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DISTRIBUTION=distribution,
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SEED=seed
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)
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bin_heap = BinHeap(nodes)
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st = timer()
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stats = heap_dijkstra(nodes, adj, bin_heap, start)
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et = timer()
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rows.append(
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{
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"nodes": nodes,
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"density": density,
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"std": std,
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"sigma": sigma,
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"trial": trial,
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"seed": seed,
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"time": et - st,
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"algorithm": "binary",
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"extract_min_calls": stats.extract_min_calls,
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"relax_attempts": stats.relax_attempts,
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"relax_success": stats.relax_success,
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}
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)
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fibo_heap = FiboHeap(nodes)
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st = timer()
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stats = heap_dijkstra(nodes, adj, fibo_heap, start)
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et = timer()
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rows.append(
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{
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"nodes": nodes,
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"density": density,
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"std": std,
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"sigma": sigma,
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"trial": trial,
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"seed": seed,
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"time": et - st,
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"algorithm": "fibonacci",
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"extract_min_calls": stats.extract_min_calls,
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"relax_attempts": stats.relax_attempts,
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"relax_success": stats.relax_success,
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}
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)
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pbar.update(1)
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df_nodes = pd.DataFrame(rows)
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df_nodes.to_csv(
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csv_file,
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mode='a',
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header=not file_exists,
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index=False
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)
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file_exists = True
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print(f"=== SAVED nodes = {nodes} ({len(df_nodes)} rows) ===")
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return
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# Settings
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nodes_li = [2000, 4000, 8000, 16000]
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densities = [0.0000001, 0.0000003, 0.000001, 0.000003, 0.00001, 0.00003, 0.0001, 0.0003, 0.001, 0.003, 0.01, 0.03, 0.1, 0.3]
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stds = [1000, 1500, 2000, 3000, 4000, 6000, 8000, 12000, 16000]
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trials = 10
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mean_dist = 3000
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start = 0
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base_seed = 42
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save_folder = "results/synthetic_data/raw"
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os.makedirs(save_folder, exist_ok=True)
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timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
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csv_file = f"{save_folder}/{timestamp}.csv"
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config_file = f"{save_folder}/{timestamp}.json"
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config = {
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"nodes_li": nodes_li,
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"densities": densities,
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"stds": stds,
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"trials": trials,
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"mean_dist": mean_dist,
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"start": start,
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"base_seed": base_seed,
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}
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with open(config_file, "w") as f:
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json.dump(config, f, indent=4)
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df = run_test(
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nodes_li = nodes_li,
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densities = densities,
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stds = stds,
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trials = trials,
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mean_dist = mean_dist,
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start = start,
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base_seed = base_seed
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) |