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test_reasoner.py
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"""
Script to test the TSP model and a combination of deterministic algorithms
Usage:
test_reasoner.py (--load-model-from LFM) [options]
Options:
-h --help Show this screen.
--load-model-from LFM Path to the model to be loaded
--seed S Random seed to set. [default: 47]
"""
import time
import os
from docopt import docopt
import schema
from collections import defaultdict
import pytorch_lightning as pl
from models.algorithm_processor import LitAlgorithmProcessor
from hyperparameters import get_hyperparameters
from datasets.constants import _DATASET_ROOTS
name_mapper = {
"bellman_ford": "Bellman-Ford",
"bfs": "BFS",
"dfs": "DFS",
"dag_shortest_paths": "DAG Shortest Paths",
"floyd_warshall": "Floyd-Warshall",
"mst_prim": "MST Prim",
"strongly_connected_components_local": "SCC",
"binary_search": "Binary Search",
"insertion_sort": "Insertion Sort",
"minimum": "Minimum",
}
run_names_baselines = [
"decent-bee-661",
"glowing-valley-660",
"super-terrain-659",
"still-sky-644",
"wise-pine-643",
"misunderstood-morning-644",
"earnest-haze-632",
"rosy-pond-631",
"scarlet-vortex-630",
"comic-shadow-620",
"firm-river-616",
"wandering-armadillo-615",
"chocolate-wind-596",
"comfy-snowball-587",
"decent-firefly-580",
"earthy-microwave-579",
"devout-rain-578",
"trim-dragon-577",
"divine-salad-574",
"fearless-feather-571",
"floral-glitter-570",
"expert-water-569",
"pious-deluge-566",
"misty-glitter-565",
"faithful-sky-534",
"lilac-lion-529",
"firm-pond-528",
"unique-dream-522",
"prime-river-521",
"fluent-wind-516",
] # baselines
run_names_deqs = [
"woven-fire-666",
"stoic-hill-665",
"spring-lion-662",
"stilted-plant-639",
"comfy-serenity-637",
"colorful-dust-636",
"summer-thunder-621",
"fearless-frog-619",
"brisk-feather-618",
"soft-oath-539",
"northern-sunset-538",
"wild-disco-537",
"fallen-glade-448",
"lilac-dew-446",
"vivid-haze-445",
"stellar-firebrand-429",
"prime-pine-414",
"brisk-pyramid-413",
"hopeful-grass-475",
"balmy-morning-341",
"trim-thunder-332",
"jumping-dew-362",
"clean-pine-363",
"dazzling-haze-361",
"glad-plasma-344",
"prime-capybara-343",
"confused-feather-342",
"unique-totem-609",
"distinctive-vortex-604",
"different-violet-603",
] # unmodified DEARs
def get_df_for_run_names(run_names, whattoget="train/loss/average_loss_epoch"):
runs = api.runs("clrs-cambridge/nardeq")
runs = list(filter(lambda x: x.name in run_names, runs))
hist_list_step = []
hist_list_epoch = []
for run in runs:
name = run.config["algorithm_names"][0]
if name == "binary_search":
continue
hist_epoch = run.history(keys=["_step", "epoch", eval(f'f"{whattoget}"')])
hist_epoch["name"] = name_mapper[name]
hist_list_epoch.append(hist_epoch)
df = pd.concat(hist_list_epoch, ignore_index=True)
return df
MODEL_PATHS = [
"./serialised_models/pretrained/BS_DEQ_seed2.ckpt",
"./serialised_models/pretrained/BFS_DEQ_seed1.ckpt",
"./serialised_models/pretrained/BS_BL_seed3.ckpt",
"./serialised_models/pretrained/BFS_BL_seed3.ckpt",
"./serialised_models/pretrained/FW_DEQ_seed2.ckpt",
"./serialised_models/pretrained/SCC_DEQ_seed2.ckpt",
"./serialised_models/pretrained/mst_prim_DEQ_seed2.ckpt",
"./serialised_models/pretrained/BFS_DEQ_seed3.ckpt",
"./serialised_models/pretrained/FW_DEQ_seed3.ckpt",
"./serialised_models/pretrained/BS_DEQ_seed1.ckpt",
"./serialised_models/pretrained/mst_prim_BL_seed1.ckpt",
"./serialised_models/pretrained/MIN_DEQ_seed2.ckpt",
"./serialised_models/pretrained/mst_prim_BL_seed2.ckpt",
"./serialised_models/pretrained/DSP_BL_seed3.ckpt",
"./serialised_models/pretrained/IS_BL_seed2.ckpt",
"./serialised_models/pretrained/IS_DEQ_seed2.ckpt",
"./serialised_models/pretrained/BS_BL_seed2.ckpt",
"./serialised_models/pretrained/DFS_DEQ_seed2.ckpt",
"./serialised_models/pretrained/BS_DEQ_seed3.ckpt",
"./serialised_models/pretrained/SCC_BL_seed1.ckpt",
"./serialised_models/pretrained/FW_BL_seed1.ckpt",
"./serialised_models/pretrained/BFS_DEQ_seed2.ckpt",
"./serialised_models/pretrained/DSP_BL_seed1.ckpt",
"./serialised_models/pretrained/DSP_BL_seed2.ckpt",
"./serialised_models/pretrained/SCC_DEQ_seed1.ckpt",
"./serialised_models/pretrained/FW_BL_seed3.ckpt",
"./serialised_models/pretrained/MIN_BL_seed1.ckpt",
"./serialised_models/pretrained/BFS_BL_seed2.ckpt",
"./serialised_models/pretrained/SCC_BL_seed3.ckpt",
"./serialised_models/pretrained/MIN_DEQ_seed3.ckpt",
"./serialised_models/pretrained/BF_BL_seed1.ckpt",
"./serialised_models/pretrained/mst_prim_DEQ_seed1.ckpt",
"./serialised_models/pretrained/BF_BL_seed3.ckpt",
"./serialised_models/pretrained/MIN_BL_seed3.ckpt",
"./serialised_models/pretrained/DSP_DEQ_seed3.ckpt",
"./serialised_models/pretrained/MIN_DEQ_seed1.ckpt",
"./serialised_models/pretrained/IS_DEQ_seed1.ckpt",
"./serialised_models/pretrained/BF_DEQ_seed1.ckpt",
"./serialised_models/pretrained/BFS_BL_seed1.ckpt",
"./serialised_models/pretrained/mst_prim_BL_seed3.ckpt",
"./serialised_models/pretrained/DFS_DEQ_seed3.ckpt",
"./serialised_models/pretrained/DSP_DEQ_seed1.ckpt",
"./serialised_models/pretrained/SCC_BL_seed2.ckpt",
"./serialised_models/pretrained/DFS_BL_seed1.ckpt",
"./serialised_models/pretrained/IS_DEQ_seed3.ckpt",
"./serialised_models/pretrained/BF_BL_seed2.ckpt",
"./serialised_models/pretrained/DFS_BL_seed2.ckpt",
"./serialised_models/pretrained/SCC_DEQ_seed3.ckpt",
"./serialised_models/pretrained/DSP_DEQ_seed2.ckpt",
"./serialised_models/pretrained/FW_DEQ_seed1.ckpt",
"./serialised_models/pretrained/mst_prim_DEQ_seed3.ckpt",
"./serialised_models/pretrained/DFS_BL_seed3.ckpt",
"./serialised_models/pretrained/DFS_DEQ_seed1.ckpt",
"./serialised_models/pretrained/BS_BL_seed1.ckpt",
"./serialised_models/pretrained/IS_BL_seed1.ckpt",
"./serialised_models/pretrained/IS_BL_seed3.ckpt",
"./serialised_models/pretrained/BF_DEQ_seed2.ckpt",
"./serialised_models/pretrained/FW_BL_seed2.ckpt",
"./serialised_models/pretrained/BF_DEQ_seed3.ckpt",
"./serialised_models/pretrained/MIN_BL_seed2.ckpt",
]
def test_model(model_path):
lit_processor = LitAlgorithmProcessor.load_from_checkpoint(
model_path,
dataset_root=_DATASET_ROOTS["mst_prim"],
strict=False,
)
start_time = time.time()
trainer = pl.Trainer(
accelerator="cuda", # Change to 'cpu' if you're not using GPU
check_val_every_n_epoch=1,
log_every_n_steps=100,
)
trainer.test(model=lit_processor)
end_time = time.time()
return (end_time - start_time) / 100.0
if __name__ == "__main__":
serialised_models_dir = os.path.abspath("./serialised_models/")
hidden_dim = get_hyperparameters()["dim_latent"]
schema = schema.Schema(
{
"--help": bool,
"--load-model-from": schema.Or(None, os.path.exists),
"--seed": schema.Use(int),
}
)
args = docopt(__doc__)
args = schema.validate(args)
testing_times_dict = defaultdict(list)
# Iterate over each model path
for model_path in MODEL_PATHS:
print("TESTING", model_path)
# Extract model type and dataset name from the path
model_type = model_path.split("/")[-1].split("_")[
1
] # Extracting model type (DEQ or BL)
dataset_name = model_path.split("/")[-1].split("_")[
0
] # Extracting dataset name
# Test the model and record testing time
testing_time = test_model(model_path)
# Store testing time for the corresponding model type and dataset
testing_times_dict[(model_type, dataset_name)].append(testing_time)
# Dictionary to store mean testing times for each combination of model type and dataset
mean_testing_times = defaultdict(dict)
std_testing_times = defaultdict(dict)
for key, times in testing_times_dict.items():
mean_time = torch.tensor(times).mean().item()
std_time = torch.tensor(times).std().item()
mean_testing_times[key[0]][key[1]] = mean_time
std_testing_times[key[0]][key[1]] = std_time
# Print mean testing times
print("MEAN:")
for model_type, dataset_times in mean_testing_times.items():
print(f"{model_type}:")
for dataset, mean_time in dataset_times.items():
print(f" {dataset}: {mean_time} seconds")
# Print standard deviation of testing times
print("\nSTD:")
for model_type, dataset_times in std_testing_times.items():
print(f"{model_type}:")
for dataset, std_time in dataset_times.items():
print(f" {dataset}: {std_time} seconds")
# lit_processor = LitAlgorithmProcessor.load_from_checkpoint(
# args['--load-model-from'],
# dataset_root=_DATASET_ROOTS['mst_prim'],
# strict=False,
# )
# trainer = pl.Trainer(
# accelerator='cuda',
# check_val_every_n_epoch=1,
# log_every_n_steps=100,
# )
# trainer.test(
# model=lit_processor,
# )