-
Notifications
You must be signed in to change notification settings - Fork 0
/
run.py
270 lines (244 loc) · 11.6 KB
/
run.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
import cProfile
import logging
import os
import pickle
import pstats
import time
from typing import Dict, List, Optional
from tqdm import tqdm
from Graph import Arena, Player, GenerationStrategy
from Solver import Solver
from plot_graph import plot_graph
import json
from argparse import ArgumentParser
def run_solver(num_nodes: Optional[int] = None,
edge_probability: Optional[float] = None,
save_results: bool = False,
save_arena: bool = False,
seed: int | None = None,
plot: bool = False,
optimize: bool = False,
arena: Optional[Arena] = None,
strategy: GenerationStrategy = GenerationStrategy.INCREMENTAL_BELLMAN_FORD):
if arena and (num_nodes or edge_probability):
raise ValueError("You must provide either an arena or the number of nodes and edge probability")
if not arena:
arena = Arena(num_nodes=num_nodes,
edge_probability=edge_probability,
seed=seed)
arena.generate(strategy)
if plot:
plot_graph(arena)
if save_arena:
arena.save(f"arena_{arena.num_nodes}_{arena.edge_probability}.pkl")
solver = Solver(arena)
start = time.time()
if optimize:
num_steps = solver.optimized_value_iteration()
else:
num_steps = solver.value_iteration()
end = time.time()
time_in_ms = (end - start) * 1000
min_energy_dict = solver.get_min_energy()
if save_results:
results = {
"time_to_complete": time_in_ms,
"steps": num_steps,
"strategy": "not_applicable",
"min_energy_min": min_energy_dict[Player.MIN],
"min_energy_max": min_energy_dict[Player.MAX],
"num_edges": len(arena.edges),
"num_nodes": arena.num_nodes,
"edge_probability": arena.edge_probability,
"optimized": optimize
}
update_json_results(
arena_name=f"arena_{arena.num_nodes}_{arena.edge_probability}",
update_dict=results,
file="solve_results.json")
return min_energy_dict
# def save_results_json(arena: Arena,
# min_energy_dict: dict,
# time_to_complete: float,
# steps:int,
# file: str,
# optimized: bool = False) -> None:
# min_energy_dict.update({"time_to_complete_ms": time_to_complete})
# min_energy_dict.update({"converged_in_steps": steps})
# min_energy_dict.update({"num_nodes": arena.num_nodes})
# min_energy_dict.update({"edge_probability": arena.edge_probability})
# min_energy_dict.update({"MAX_nodes": len([node for node, player in arena.player_mapping.items() if player == Player.MIN])})
# min_energy_dict.update({"MIN_nodes": len([node for node, player in arena.player_mapping.items() if player == Player.MAX])})
# min_energy_dict.update({"edges": len(arena.edges)})
# min_energy_dict.update({"sum_player_MAX_weights": sum([arena.value_mapping[node] for node, player in arena.player_mapping.items() if player == Player.MAX])})
# min_energy_dict.update({"sum_player_MIN_weights": sum([arena.value_mapping[node] for node, player in arena.player_mapping.items() if player == Player.MIN])})
# min_energy_dict.update({"min_energy_MAX": min_energy_dict[Player.MAX]})
# min_energy_dict.update({"min_energy_MIN": min_energy_dict[Player.MIN]})
# min_energy_dict.update({"optimized": optimized})
# del min_energy_dict[Player.MAX]
# del min_energy_dict[Player.MIN]
# base_path = "results"
# with open(os.path.join(base_path, file), "w") as f:
# json.dump(min_energy_dict, f)
def new_save_results(update_dict: Dict[str, any], file: str) -> None:
pass
def profile(seed: int = 0,
plot: bool = False,
save: bool = False,
optimize: bool = False,
strategy: GenerationStrategy = GenerationStrategy.INCREMENTAL_BELLMAN_FORD):
profiler = cProfile.Profile()
profiler.enable()
# Run your function
arena = Arena().load("arenas/arena_1000_0.1.pkl")
solution = run_solver(seed=seed, plot=plot, save_arena=save, arena=arena, optimize=optimize, strategy=strategy)
logging.info(f"Solution: {solution}")
profiler.disable()
stats = pstats.Stats(profiler).sort_stats('cumtime')
stats.print_stats()
def generate_arenas(nodes_space: Optional[List[int]] = None,
probability_space: Optional[List[float]] = None,
seed: int | None = None,
strategy: GenerationStrategy = GenerationStrategy.INCREMENTAL_BELLMAN_FORD):
if not nodes_space:
nodes_space = [10, 100, 500, 1000, 5000, 10_000]
if not probability_space:
probability_space = [0.01, 0.05, 0.1, 0.2, 0.5]
pbar = tqdm(total=len(nodes_space) * len(probability_space), desc="Generating arena")
for n in nodes_space:
for p in probability_space:
pbar.set_description(f"Generating arena_{n}_{p}")
if os.path.exists(f"arenas/arena_{n}_{p}.pkl"):
pbar.update(1)
continue
arena = Arena(num_nodes=n, edge_probability=p, seed=seed)
start = time.time()
arena.generate(strategy)
end = time.time()
time_to_generate = (end - start) * 1000
arena_name = f"arena_{n}_{p}"
arena.save(f"arenas/{arena_name}.pkl")
if not os.path.exists("arena_times.json"):
print(f"Creating arena_times.json for the first time")
with open("arena_times.json", "w") as f:
json.dump({}, f)
with open("arena_times.json", "r") as f:
data = json.load(f)
if arena_name not in data:
data[arena_name] = [{"time_to_generate": time_to_generate,
"strategy": strategy.value,
"num_nodes": n,
"num_edges": len(arena.edges),
"edge_probability": p}]
else:
data[arena_name].append({"time_to_generate": time_to_generate,
"strategy": strategy.value,
"num_nodes": n,
"num_edges": len(arena.edges),
"edge_probability": p})
with open("arena_times.json", "w") as f:
json.dump(data, f)
pbar.update(1)
def generate_arena(num_nodes: Optional[int] = None,
edge_probability: Optional[float] = None,
seed: Optional[int] = None,
strategy: GenerationStrategy = GenerationStrategy.INCREMENTAL_BELLMAN_FORD,
save_arena: bool = False,
plot: bool = False):
arena = Arena(num_nodes=num_nodes, edge_probability=edge_probability, seed=seed)
start = time.time()
arena.generate(strategy)
end = time.time()
time_to_generate = (end - start) * 1000
arena_name = f"arena_{num_nodes}_{edge_probability}"
if save_arena:
arena.save(f"arenas/{arena_name}.pkl")
if plot:
plot_graph(arena)
update_json_results(arena_name=arena_name,
update_dict={"time_to_generate": time_to_generate,
"strategy": strategy,
"num_nodes": num_nodes,
"num_edges": len(arena.edges),
"edge_probability": edge_probability},
file="arena_times.json")
def update_json_results(arena_name: str, update_dict: Dict[str, any], file: str) -> None:
if "strategy" not in update_dict:
raise ValueError("You must provide a strategy in the update_dict")
if not os.path.exists(file):
print(f"Creating arena_times.json for the first time")
with open(file, "w") as f:
json.dump({}, f)
with open(file, "r") as f:
data = json.load(f)
if arena_name not in data:
data[arena_name] = {update_dict["strategy"]: [update_dict]}
else:
if update_dict["strategy"] not in data[arena_name]:
data[arena_name][update_dict["strategy"]] = [update_dict]
else:
data[arena_name][update_dict["strategy"]].append(update_dict)
with open(file, "w") as f:
json.dump(data, f, indent=4)
print("RESULTS UPDATED")
if __name__ == "__main__":
parser = ArgumentParser()
# Generate
parser.add_argument("--batch-generate", action="store_true")
parser.add_argument("--generate", action="store_true")
parser.add_argument("--node-space", dest="node_space", type=int, nargs="+")
parser.add_argument("--probability-space", dest="probability_space", type=float, nargs="+")
parser.add_argument("--strategy", dest="strategy", type=str, default="incremental_bellman_ford") #possible strategies defined in Graph.GenerationStrategy
# Solve
parser.add_argument("--solve", action="store_true")
parser.add_argument("--num-nodes", dest="num_nodes", type=int, default=100)
parser.add_argument("--edge-probability", dest="edge_probability", type=float, default=0.4)
parser.add_argument("--seed", dest="seed", type=int, default=0)
parser.add_argument("--plot", dest="plot", action="store_true")
parser.add_argument("--save-arena", dest="save_arena", action="store_true")
parser.add_argument("--save-results", dest="save_results", action="store_true")
parser.add_argument("--optimize", dest="optimize", action="store_true")
parser.add_argument("--arena-path", dest="arena_path", type=str, default=None)
# Profile (for debug)
parser.add_argument("--profile", action="store_true")
args = parser.parse_args()
if args.solve:
if args.arena_path:
with open(args.arena_path, "rb") as f:
arena = pickle.load(f)
result = run_solver(arena=arena,
plot=args.plot,
save_arena=args.save_arena,
optimize=args.optimize,
save_results=args.save_results,
strategy=args.strategy)
else:
result = run_solver(num_nodes=args.num_nodes,
edge_probability=args.edge_probability,
save_results=args.save_results,
seed=args.seed,
plot=args.plot,
save_arena=args.save_arena,
optimize=args.optimize,
strategy=args.strategy)
print(result)
elif args.batch_generate:
generate_arenas(nodes_space=args.node_space,
probability_space=args.probability_space,
seed=args.seed,
strategy=args.strategy)
elif args.generate:
generate_arena(seed=args.seed,
num_nodes=args.num_nodes,
edge_probability=args.edge_probability,
strategy=args.strategy,
save_arena=args.save_arena,
plot=args.plot)
elif args.profile:
profile(seed=args.seed,
plot=args.plot,
save=args.save_results,
optimize=args.optimize,
strategy=args.strategy)
else:
raise ValueError("You must provide either --generate or --solve")