def perturb_all_nodes(variant):
runner, ckpt_path = init(variant, device=device)
experiments = {
# "dropout": {
# "dropout": True,
# },
"pulse1": {
"strength": 1.0
},
"pulse10": {
"strength": 10.0
},
# "pulse100": {
# "strength": 100.0
# },
# "pulse1000": {
# "strength": 1000.0
# },
}
for experiment_name in experiments:
experiment_args = experiments[experiment_name]
reset_random_state(runner.config)
# 10 nodes, 10 timesteps, 5 random draws
node_range = torch.arange(runner.config.TASK.NUM_NODES)
# node_range = torch.randint(0, runner.config.TASK.NUM_NODES, (settings,))
step_range = [5, runner.config.TASK.NUM_STEPS // 2, runner.config.TASK.NUM_STEPS - 5] # Early, mid, late
perturb_exp = []
print()
print(f"{runner.config.VARIANT} - {experiment_name}")
print()
for step in step_range:
for node in node_range:
_, primary_res = pulse(
runner,
ckpt_path,
trials=5,
step=step,
node=[node],
**experiment_args
)
perturb_exp.append({
"step": step,
"node": node,
"score": torch.tensor(primary_res).mean()
})
torch.save(perturb_exp, f"./eval/perturb_all_{runner.config.VARIANT}-{experiment_name}.pth")
device = torch.device("cuda", 0)
else:
device = torch.device("cpu")
import torch.nn.functional as f
from torch.utils import data
from analyze_utils import init
from analyze_utils import init, pulse, reset_random_state #, rq_1_1
variant = "seq_mnist_drop0"
variant = "seq_mnist"
runner, ckpt_path = init(variant, device=device)
#%%
import networkx as nx
G = nx.read_edgelist(runner.config.MODEL.GRAPH_FILE)
nx.draw(G, with_labels=True)
# plt.savefig("network.pdf")
print(nx.diameter(G))
#%%
metrics, info = runner.eval(ckpt_path)
inputs = info["inputs"]
outputs = info["outputs"]
targets = info["targets"]
masks = info["masks"]
#%%
import seaborn as sns
import numpy as np
# ! SET YOUR DEVICE HERE
ALLOCATED_DEVICE_ID = 4
os.environ["CUDA_VISIBLE_DEVICES"] = str(ALLOCATED_DEVICE_ID)
import torch
if torch.cuda.device_count() >= 0:
device = torch.device("cuda", 0)
else:
device = torch.device("cpu")
from analyze_utils import init, pulse, reset_random_state # , rq_1_1
variant = "dc"
runner, ckpt_path = init(variant)
#%%
import networkx as nx
G = nx.read_edgelist(runner.config.MODEL.GRAPH_FILE)
nx.draw(G, with_labels=True)
# plt.savefig("network.pdf")
print(nx.diameter(G))
#%%
metrics, info = runner.eval(ckpt_path)
inputs, outputs, targets, masks = info
inputs = info["inputs"]
outputs = info["outputs"]