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launch_class_separator_exp.py
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"""
On Class Distributions Induced by Nearest Neighbor Graphs for Node Classification of Tabular Data
File: launch_class_separator_exp.py
Authors: Federico Errica (federico.errica@neclab.eu)
NEC Laboratories Europe GmbH, Copyright (c) 2023, All rights reserved.
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"""
import os
import torch
from torch.optim import SGD, Adam
from class_separator import ClassSeparator
from tqdm import tqdm
def main():
seed = 42
torch.manual_seed(seed)
device = "cuda:0"
for num_features in [1, 2, 5, 10, 50]:
for num_classes in [2, 5]:
for num_mixtures in [2, 5, 10]:
# lambda1 controls the importance of the CCNS lower bound term
# minimizing deltaSED might lead to different class distributions
# being completely identical, and we want to push the model away
# from a naive solution that possibly represents a local minimum
# in the loss landscape.
for lambda1 in [0.0, 1.0, 5.0, 10.0]:
retry = True
while retry:
retry = False
try:
model_ckpt_pth = (
f"C_{num_classes}_M_{num_mixtures}_"
f"D_{num_features}_"
f"lambda_{lambda1}_"
f"seed_{seed}.pth"
)
results_ckpt_pth = (
f"C_{num_classes}_M_{num_mixtures}_"
f"D_{num_features}_"
f"lambda_{lambda1}_"
f"seed_{seed}.pt"
)
if os.path.exists(
os.path.join("RESULTS", results_ckpt_pth)
):
print(
"Skipping configuration due to existing results"
)
continue
# k, sigma, epsilon are optimized by the method
model = ClassSeparator(
num_classes=num_classes,
num_mixtures=num_mixtures,
num_features=num_features,
use_full_covariance=(
lambda1 == 0.0 and num_features > 1
),
)
epochs = 10000
patience = 3000
best_epoch = 0
optimizer = Adam(
params=model.parameters(), lr=0.01
)
load_checkpoint = False
if load_checkpoint:
p = os.path.join(
"./checkpoints", model_ckpt_pth
)
if os.path.exists(p):
model.load_state_dict(
torch.load(
p,
map_location="cpu",
)
)
model.to(device)
model.train()
print(
f"Start training with D={num_features}, "
f"|C|={num_classes}, |M|={num_mixtures}, "
f"lambda={lambda1}..."
)
with tqdm(total=epochs) as tepoch:
best_deltaSED = torch.inf
for epoch in range(1, epochs + 1):
optimizer.zero_grad()
sed_tuples, lb_ccns, m_cc = model()
deltaSED = 0.0
# combine all SED differences for each pair of c,c'
for s in sed_tuples:
# We want to maximize SED_H - SED_X,
# so we append a minus and minimize deltaSED
deltaSED -= s[0]
if lambda1 != 0.0:
# compute the regularizer based on lower
# bound of the CCNS
loss_lb_ccns = 0.0
for c_prime in range(model.C):
for c in range(model.C):
if c_prime == c:
# maximize intra-class ccns
loss_lb_ccns -= lb_ccns[
c, c_prime
]
else:
# minimize inter-class ccns
loss_lb_ccns += lb_ccns[
c, c_prime
]
loss = (
deltaSED + lambda1 * loss_lb_ccns
)
else:
loss = deltaSED
loss.backward()
optimizer.step()
tepoch.set_description(f"Epoch {epoch}")
tepoch.set_postfix(
loss=loss.item(),
deltaSED=deltaSED.item(),
)
tepoch.update(1)
if deltaSED.item() < best_deltaSED:
best_epoch = epoch
best_deltaSED = (
deltaSED.detach().item()
)
best_lb_ccns = (
lb_ccns.detach()
if lambda1 > 0.0
else None
)
best_sed_tuples = (
[
(t[1], t[2], t[3], t[4])
for t in sed_tuples
],
)
if not os.path.exists("./checkpoints"):
os.makedirs("./checkpoints")
torch.save(
model.state_dict(),
"./checkpoints/" + model_ckpt_pth,
)
if epoch - best_epoch > patience:
print(
f"Stopping training - early stopping"
)
break
if not os.path.exists("./RESULTS"):
os.makedirs("./RESULTS")
torch.save(
{
"best_epoch": best_epoch,
"best_sed_tuples": best_sed_tuples,
"best_deltaSED": best_deltaSED,
"best_lb_ccns": best_lb_ccns,
"C": num_classes,
"M": num_mixtures,
"D": num_features,
"lambda": lambda1,
"seed": seed,
},
os.path.join("RESULTS", results_ckpt_pth),
)
print(f"Training of configuration has finished.")
except Exception as e:
print("Exception caught, retying...")
retry = True
if __name__ == "__main__":
main()