fedex/mt
Public Access
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

black formatted files before changes

Browse Source
This commit is contained in:
Jan Kowalczyk
2024-06-28 11:36:46 +02:00
parent d33c6b1e16
commit 71f9662022
40 changed files with 2938 additions and 1260 deletions
Download Patch File Download Diff File Expand all files Collapse all files

103
Deep-SAD-PyTorch/src/baselines/SemiDGM.py
View File

@@ -36,17 +36,13 @@ class SemiDeepGenerativeModel(object):
self.vae_optimizer_name = None
self.results = {
'train_time': None,
'test_auc': None,
'test_time': None,
'test_scores': None,
"train_time": None,
"test_auc": None,
"test_time": None,
"test_scores": None,
}
self.vae_results = {
'train_time': None,
'test_auc': None,
'test_time': None
}
self.vae_results = {"train_time": None, "test_auc": None, "test_time": None}
def set_vae(self, net_name):
"""Builds the variational autoencoder network for pretraining."""
@@ -58,71 +54,106 @@ class SemiDeepGenerativeModel(object):
self.net_name = net_name
self.net = build_network(net_name, ae_net=self.vae_net) # full M1+M2 model
def train(self, dataset: BaseADDataset, optimizer_name: str = 'adam', lr: float = 0.001, n_epochs: int = 50,
lr_milestones: tuple = (), batch_size: int = 128, weight_decay: float = 1e-6, device: str = 'cuda',
n_jobs_dataloader: int = 0):
def train(
self,
dataset: BaseADDataset,
optimizer_name: str = "adam",
lr: float = 0.001,
n_epochs: int = 50,
lr_milestones: tuple = (),
batch_size: int = 128,
weight_decay: float = 1e-6,
device: str = "cuda",
n_jobs_dataloader: int = 0,
):
"""Trains the Semi-Supervised Deep Generative model on the training data."""
self.optimizer_name = optimizer_name
self.trainer = SemiDeepGenerativeTrainer(alpha=self.alpha, optimizer_name=optimizer_name, lr=lr,
n_epochs=n_epochs, lr_milestones=lr_milestones, batch_size=batch_size,
weight_decay=weight_decay, device=device,
n_jobs_dataloader=n_jobs_dataloader)
self.trainer = SemiDeepGenerativeTrainer(
alpha=self.alpha,
optimizer_name=optimizer_name,
lr=lr,
n_epochs=n_epochs,
lr_milestones=lr_milestones,
batch_size=batch_size,
weight_decay=weight_decay,
device=device,
n_jobs_dataloader=n_jobs_dataloader,
)
self.net = self.trainer.train(dataset, self.net)
self.results['train_time'] = self.trainer.train_time
self.results["train_time"] = self.trainer.train_time
def test(self, dataset: BaseADDataset, device: str = 'cuda', n_jobs_dataloader: int = 0):
def test(
self, dataset: BaseADDataset, device: str = "cuda", n_jobs_dataloader: int = 0
):
"""Tests the Semi-Supervised Deep Generative model on the test data."""
if self.trainer is None:
self.trainer = SemiDeepGenerativeTrainer(alpha=self.alpha, device=device,
n_jobs_dataloader=n_jobs_dataloader)
self.trainer = SemiDeepGenerativeTrainer(
alpha=self.alpha, device=device, n_jobs_dataloader=n_jobs_dataloader
)
self.trainer.test(dataset, self.net)
# Get results
self.results['test_auc'] = self.trainer.test_auc
self.results['test_time'] = self.trainer.test_time
self.results['test_scores'] = self.trainer.test_scores
self.results["test_auc"] = self.trainer.test_auc
self.results["test_time"] = self.trainer.test_time
self.results["test_scores"] = self.trainer.test_scores
def pretrain(self, dataset: BaseADDataset, optimizer_name: str = 'adam', lr: float = 0.001, n_epochs: int = 100,
lr_milestones: tuple = (), batch_size: int = 128, weight_decay: float = 1e-6, device: str = 'cuda',
n_jobs_dataloader: int = 0):
def pretrain(
self,
dataset: BaseADDataset,
optimizer_name: str = "adam",
lr: float = 0.001,
n_epochs: int = 100,
lr_milestones: tuple = (),
batch_size: int = 128,
weight_decay: float = 1e-6,
device: str = "cuda",
n_jobs_dataloader: int = 0,
):
"""Pretrains a variational autoencoder (M1) for the Semi-Supervised Deep Generative model."""
# Train
self.vae_optimizer_name = optimizer_name
self.vae_trainer = VAETrainer(optimizer_name=optimizer_name, lr=lr, n_epochs=n_epochs,
lr_milestones=lr_milestones, batch_size=batch_size, weight_decay=weight_decay,
device=device, n_jobs_dataloader=n_jobs_dataloader)
self.vae_trainer = VAETrainer(
optimizer_name=optimizer_name,
lr=lr,
n_epochs=n_epochs,
lr_milestones=lr_milestones,
batch_size=batch_size,
weight_decay=weight_decay,
device=device,
n_jobs_dataloader=n_jobs_dataloader,
)
self.vae_net = self.vae_trainer.train(dataset, self.vae_net)
# Get train results
self.vae_results['train_time'] = self.vae_trainer.train_time
self.vae_results["train_time"] = self.vae_trainer.train_time
# Test
self.vae_trainer.test(dataset, self.vae_net)
# Get test results
self.vae_results['test_auc'] = self.vae_trainer.test_auc
self.vae_results['test_time'] = self.vae_trainer.test_time
self.vae_results["test_auc"] = self.vae_trainer.test_auc
self.vae_results["test_time"] = self.vae_trainer.test_time
def save_model(self, export_model):
"""Save a Semi-Supervised Deep Generative model to export_model."""
net_dict = self.net.state_dict()
torch.save({'net_dict': net_dict}, export_model)
torch.save({"net_dict": net_dict}, export_model)
def load_model(self, model_path):
"""Load a Semi-Supervised Deep Generative model from model_path."""
model_dict = torch.load(model_path)
self.net.load_state_dict(model_dict['net_dict'])
self.net.load_state_dict(model_dict["net_dict"])
def save_results(self, export_json):
"""Save results dict to a JSON-file."""
with open(export_json, 'w') as fp:
with open(export_json, "w") as fp:
json.dump(self.results, fp)
def save_vae_results(self, export_json):
"""Save variational autoencoder results dict to a JSON-file."""
with open(export_json, 'w') as fp:
with open(export_json, "w") as fp:
json.dump(self.vae_results, fp)