ocsvm working
This commit is contained in:
Jan Kowalczyk
@@ -167,6 +167,12 @@ from utils.visualization.plot_images_grid import plot_images_grid
|
||||
default=1e-6,
|
||||
help="Weight decay (L2 penalty) hyperparameter for Deep SAD objective.",
|
||||
)
|
||||
@click.option(
|
||||
"--latent_space_dim",
|
||||
type=int,
|
||||
default=128,
|
||||
help="Dimensionality of the latent space for the autoencoder.",
|
||||
)
|
||||
@click.option(
|
||||
"--pretrain",
|
||||
type=bool,
|
||||
@@ -303,6 +309,7 @@ def main(
|
||||
lr_milestone,
|
||||
batch_size,
|
||||
weight_decay,
|
||||
latent_space_dim,
|
||||
pretrain,
|
||||
ae_optimizer_name,
|
||||
ae_lr,
|
||||
@@ -415,7 +422,7 @@ def main(
|
||||
train_passes = range(k_fold_num) if k_fold else [None]
|
||||
|
||||
train_isoforest = True
|
||||
train_ocsvm = False
|
||||
train_ocsvm = True
|
||||
train_deepsad = True
|
||||
|
||||
for fold_idx in train_passes:
|
||||
@@ -424,10 +431,6 @@ def main(
|
||||
else:
|
||||
logger.info(f"Fold {fold_idx + 1}/{k_fold_num}")
|
||||
|
||||
# Initialize OC-SVM model
|
||||
if train_ocsvm:
|
||||
ocsvm = OCSVM(kernel=ocsvm_kernel, nu=ocsvm_nu, hybrid=False)
|
||||
|
||||
# Initialize Isolation Forest model
|
||||
if train_isoforest:
|
||||
Isoforest = IsoForest(
|
||||
@@ -441,7 +444,7 @@ def main(
|
||||
|
||||
# Initialize DeepSAD model and set neural network phi
|
||||
if train_deepsad:
|
||||
deepSAD = DeepSAD(cfg.settings["eta"])
|
||||
deepSAD = DeepSAD(latent_space_dim, cfg.settings["eta"])
|
||||
deepSAD.set_network(net_name)
|
||||
|
||||
# If specified, load Deep SAD model (center c, network weights, and possibly autoencoder weights)
|
||||
@@ -486,11 +489,32 @@ def main(
|
||||
|
||||
# Save pretraining results
|
||||
if fold_idx is None:
|
||||
deepSAD.save_ae_results(export_json=xp_path + "/ae_results.json")
|
||||
deepSAD.save_ae_results(export_pkl=xp_path + "/ae_results.pkl")
|
||||
ae_model_path = xp_path + "/ae_model.tar"
|
||||
deepSAD.save_model(export_model=ae_model_path, save_ae=True)
|
||||
else:
|
||||
deepSAD.save_ae_results(
|
||||
export_json=xp_path + f"/ae_results_{fold_idx}.json"
|
||||
export_pkl=xp_path + f"/ae_results_{fold_idx}.pkl"
|
||||
)
|
||||
ae_model_path = xp_path + f"/ae_model_{fold_idx}.tar"
|
||||
deepSAD.save_model(export_model=ae_model_path, save_ae=True)
|
||||
|
||||
# Initialize OC-SVM model (after pretraining to use autoencoder features)
|
||||
if train_ocsvm:
|
||||
ocsvm = OCSVM(
|
||||
kernel=ocsvm_kernel,
|
||||
nu=ocsvm_nu,
|
||||
hybrid=True,
|
||||
latent_space_dim=latent_space_dim,
|
||||
)
|
||||
if load_model and not pretrain:
|
||||
ae_model_path = load_model
|
||||
ocsvm.load_ae(
|
||||
net_name=net_name, model_path=ae_model_path, device=device
|
||||
)
|
||||
logger.info(
|
||||
f"Loaded pretrained autoencoder for features from {ae_model_path}."
|
||||
)
|
||||
|
||||
# Log training details
|
||||
logger.info("Training optimizer: %s" % cfg.settings["optimizer_name"])
|
||||
@@ -525,7 +549,7 @@ def main(
|
||||
device=device,
|
||||
n_jobs_dataloader=n_jobs_dataloader,
|
||||
k_fold_idx=fold_idx,
|
||||
batch_size=8,
|
||||
batch_size=256,
|
||||
)
|
||||
|
||||
# Train model on dataset
|
||||
@@ -553,7 +577,7 @@ def main(
|
||||
device=device,
|
||||
n_jobs_dataloader=n_jobs_dataloader,
|
||||
k_fold_idx=fold_idx,
|
||||
batch_size=8,
|
||||
batch_size=256,
|
||||
)
|
||||
|
||||
# Test model
|
||||
@@ -730,7 +754,7 @@ def main(
|
||||
logger.info("Known anomaly classes: %s" % (dataset.known_outlier_classes,))
|
||||
|
||||
# Initialize DeepSAD model and set neural network phi
|
||||
deepSAD = DeepSAD(cfg.settings["eta"])
|
||||
deepSAD = DeepSAD(latent_space_dim, cfg.settings["eta"])
|
||||
deepSAD.set_network(net_name)
|
||||
|
||||
# If specified, load Deep SAD model (center c, network weights, and possibly autoencoder weights)
|
||||
@@ -776,54 +800,87 @@ def main(
|
||||
ratio_known_outlier,
|
||||
ratio_pollution,
|
||||
random_state=np.random.RandomState(cfg.settings["seed"]),
|
||||
k_fold_num=k_fold_num,
|
||||
)
|
||||
|
||||
# Dictionary to store results for each dimension
|
||||
# ae_elbow_dims = [32, 64, 128, 256, 384, 512, 768, 1024]
|
||||
ae_elbow_dims = [32, 64]
|
||||
elbow_results = {"dimensions": list(ae_elbow_dims), "ae_results": {}}
|
||||
# Set up k-fold passes
|
||||
train_passes = range(k_fold_num) if k_fold else [None]
|
||||
|
||||
# Test dimensions
|
||||
ae_elbow_dims = [32, 64, 128, 256, 384, 512, 768, 1024]
|
||||
|
||||
# Test each dimension
|
||||
for rep_dim in ae_elbow_dims:
|
||||
logger.info(f"Testing autoencoder with latent dimension: {rep_dim}")
|
||||
|
||||
# Initialize DeepSAD model with current dimension
|
||||
deepSAD = DeepSAD(cfg.settings["eta"])
|
||||
deepSAD.set_network(
|
||||
net_name, rep_dim=rep_dim
|
||||
) # Pass rep_dim to network builder
|
||||
# Results dictionary for this dimension
|
||||
dim_results = {
|
||||
"dimension": rep_dim,
|
||||
"ae_results": {},
|
||||
"k_fold": k_fold,
|
||||
"k_fold_num": k_fold_num,
|
||||
}
|
||||
|
||||
# Pretrain autoencoder with current dimension
|
||||
deepSAD.pretrain(
|
||||
dataset,
|
||||
optimizer_name=cfg.settings["ae_optimizer_name"],
|
||||
lr=cfg.settings["ae_lr"],
|
||||
n_epochs=cfg.settings["ae_n_epochs"],
|
||||
lr_milestones=cfg.settings["ae_lr_milestone"],
|
||||
batch_size=cfg.settings["ae_batch_size"],
|
||||
weight_decay=cfg.settings["ae_weight_decay"],
|
||||
device=device,
|
||||
n_jobs_dataloader=n_jobs_dataloader,
|
||||
# For each fold
|
||||
for fold_idx in train_passes:
|
||||
if fold_idx is None:
|
||||
logger.info(f"Dimension {rep_dim}: Single training without k-fold")
|
||||
else:
|
||||
logger.info(
|
||||
f"Dimension {rep_dim}: Fold {fold_idx + 1}/{k_fold_num}"
|
||||
)
|
||||
|
||||
# Initialize DeepSAD model with current dimension
|
||||
deepSAD = DeepSAD(rep_dim, cfg.settings["eta"])
|
||||
deepSAD.set_network(net_name)
|
||||
|
||||
# Pretrain autoencoder with current dimension
|
||||
deepSAD.pretrain(
|
||||
dataset,
|
||||
optimizer_name=cfg.settings["ae_optimizer_name"],
|
||||
lr=cfg.settings["ae_lr"],
|
||||
n_epochs=cfg.settings["ae_n_epochs"],
|
||||
lr_milestones=cfg.settings["ae_lr_milestone"],
|
||||
batch_size=cfg.settings["ae_batch_size"],
|
||||
weight_decay=cfg.settings["ae_weight_decay"],
|
||||
device=device,
|
||||
n_jobs_dataloader=n_jobs_dataloader,
|
||||
k_fold_idx=fold_idx,
|
||||
)
|
||||
|
||||
# Store results for this fold
|
||||
fold_key = "single" if fold_idx is None else f"fold_{fold_idx}"
|
||||
dim_results["ae_results"][fold_key] = deepSAD.ae_results
|
||||
|
||||
logger.info(
|
||||
f"Finished testing dimension {rep_dim} "
|
||||
+ (
|
||||
f"fold {fold_idx + 1}/{k_fold_num}"
|
||||
if fold_idx is not None
|
||||
else "single pass"
|
||||
)
|
||||
)
|
||||
|
||||
# Clear some memory
|
||||
del deepSAD
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
# Save results for this dimension (includes all folds)
|
||||
results_filename = (
|
||||
f"ae_elbow_results_{net_name}_dim_{rep_dim}"
|
||||
+ ("_kfold" if k_fold else "")
|
||||
+ ".pkl"
|
||||
)
|
||||
results_path = Path(xp_path) / results_filename
|
||||
|
||||
# Store results for this dimension
|
||||
elbow_results["ae_results"][rep_dim] = deepSAD.ae_results
|
||||
with open(results_path, "wb") as f:
|
||||
pickle.dump(dim_results, f)
|
||||
|
||||
logger.info(f"Finished testing dimension {rep_dim}")
|
||||
|
||||
# Clear some memory
|
||||
del deepSAD
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
# Save all results
|
||||
results_path = Path(xp_path) / f"ae_elbow_results_{net_name}.pkl"
|
||||
with open(results_path, "wb") as f:
|
||||
pickle.dump(elbow_results, f)
|
||||
|
||||
logger.info(f"Saved elbow test results to {results_path}")
|
||||
|
||||
else:
|
||||
logger.error(f"Unknown action: {action}")
|
||||
logger.info(
|
||||
f"Saved elbow test results for dimension {rep_dim} to {results_path}"
|
||||
)
|
||||
else:
|
||||
logger.error(f"Unknown action: {action}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
Reference in New Issue
Block a user