def fit(self, X, y=None):
if issparse(X):
X = X.todense()
ds = TensorDataset(torch.from_numpy(X.astype(np.float32)))
self.autoencoder = StackedDenoisingAutoEncoder(self.dimensions, final_activation=self.final_activation)
if self.cuda:
self.autoencoder.cuda()
ae.pretrain(
ds,
self.autoencoder,
cuda=self.cuda,
epochs=self.pretrain_epochs,
batch_size=self.batch_size,
optimizer=self.optimiser_pretrain,
scheduler=self.scheduler,
corruption=0.2,
silent=True
)
ae_optimizer = self.optimiser_train(self.autoencoder)
ae.train(
ds,
self.autoencoder,
cuda=self.cuda,
epochs=self.finetune_epochs,
batch_size=self.batch_size,
optimizer=ae_optimizer,
scheduler=self.scheduler(ae_optimizer),
corruption=self.corruption,
silent=True
)
return self
def pretrain(
dataset,
autoencoder: StackedDenoisingAutoEncoder,
epochs: int,
batch_size: int,
optimizer: Callable[[torch.nn.Module], torch.optim.Optimizer],
scheduler: Optional[Callable[[torch.optim.Optimizer], Any]] = None,
validation: Optional[torch.utils.data.Dataset] = None,
corruption: Optional[float] = None,
cuda: bool = True,
sampler: Optional[torch.utils.data.sampler.Sampler] = None,
silent: bool = False,
update_freq: Optional[int] = 1,
update_callback: Optional[Callable[[float, float], None]] = None,
num_workers: Optional[int] = None,
epoch_callback: Optional[Callable[[int, torch.nn.Module], None]] = None,
) -> None:
"""
Given an autoencoder, train it using the data provided in the dataset; for simplicity the accuracy is reported only
on the training dataset. If the training dataset is a 2-tuple or list of (feature, prediction), then the prediction
is stripped away.
:param dataset: instance of Dataset to use for training
:param autoencoder: instance of an autoencoder to train
:param epochs: number of training epochs
:param batch_size: batch size for training
:param corruption: proportion of masking corruption to apply, set to None to disable, defaults to None
:param optimizer: function taking model and returning optimizer
:param scheduler: function taking optimizer and returning scheduler, or None to disable
:param validation: instance of Dataset to use for validation
:param cuda: whether CUDA is used, defaults to True
:param sampler: sampler to use in the DataLoader, defaults to None
:param silent: set to True to prevent printing out summary statistics, defaults to False
:param update_freq: frequency of batches with which to update counter, None disables, default 1
:param update_callback: function of loss and validation loss to update
:param num_workers: optional number of workers to use for data loading
:param epoch_callback: function of epoch and model
:return: None
"""
current_dataset = dataset
current_validation = validation
number_of_subautoencoders = len(autoencoder.dimensions) - 1
for index in range(number_of_subautoencoders):
encoder, decoder = autoencoder.get_stack(index)
embedding_dimension = autoencoder.dimensions[index]
hidden_dimension = autoencoder.dimensions[index + 1]
# manual override to prevent corruption for the last subautoencoder
if index == (number_of_subautoencoders - 1):
corruption = None
# initialise the subautoencoder
sub_autoencoder = DenoisingAutoencoder(
embedding_dimension=embedding_dimension,
hidden_dimension=hidden_dimension,
activation=torch.nn.ReLU()
if index != (number_of_subautoencoders - 1)
else None,
corruption=nn.Dropout(corruption) if corruption is not None else None,
)
if cuda:
sub_autoencoder = sub_autoencoder.cuda()
ae_optimizer = optimizer(sub_autoencoder)
ae_scheduler = scheduler(ae_optimizer) if scheduler is not None else scheduler
train(
current_dataset,
sub_autoencoder,
epochs,
batch_size,
ae_optimizer,
validation=current_validation,
corruption=None, # already have dropout in the DAE
scheduler=ae_scheduler,
cuda=cuda,
sampler=sampler,
silent=silent,
update_freq=update_freq,
update_callback=update_callback,
num_workers=num_workers,
epoch_callback=epoch_callback,
)
# copy the weights
sub_autoencoder.copy_weights(encoder, decoder)
# pass the dataset through the encoder part of the subautoencoder
if index != (number_of_subautoencoders - 1):
current_dataset = TensorDataset(
predict(
current_dataset,
sub_autoencoder,
batch_size,
cuda=cuda,
silent=silent,
)
)
if current_validation is not None:
current_validation = TensorDataset(
predict(
current_validation,
sub_autoencoder,
batch_size,
cuda=cuda,
silent=silent,
)
)
else:
current_dataset = None # minor optimisation on the last subautoencoder
current_validation = None
def main(data_dir, cuda, batch_size, pretrain_epochs, finetune_epochs,
testing_mode):
writer = SummaryWriter() # create the TensorBoard object
# callback function to call during training, uses writer from the scope
def training_callback(epoch, lr, loss, validation_loss):
writer.add_scalars('data/autoencoder', {
'lr': lr,
'loss': loss,
'validation_loss': validation_loss,
}, epoch)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
ds_train = CachedMNIST(data_dir,
is_train=True,
device=device,
testing_mode=testing_mode) # training dataset
ds_val = CachedMNIST(data_dir,
is_train=False,
device=device,
testing_mode=testing_mode) # evaluation dataset
autoencoder = StackedDenoisingAutoEncoder([28 * 28, 500, 500, 2000, 10],
final_activation=None)
autoencoder = autoencoder.to(device)
print('Pretraining stage.')
ae.pretrain(
ds_train,
autoencoder,
device=device,
validation=ds_val,
epochs=pretrain_epochs,
batch_size=batch_size,
silent=True,
optimizer=lambda model: SGD(model.parameters(), lr=0.1, momentum=0.9),
scheduler=lambda x: StepLR(x, 20000, gamma=0.1),
corruption=0.2)
print('Training stage.')
ae_optimizer = SGD(params=autoencoder.parameters(), lr=0.1, momentum=0.9)
ae.train(ds_train,
autoencoder,
device=device,
validation=ds_val,
epochs=finetune_epochs,
batch_size=batch_size,
silent=True,
optimizer=ae_optimizer,
scheduler=StepLR(ae_optimizer, 20000, gamma=0.1),
corruption=0.2,
update_callback=training_callback)
print('DEC stage.')
model = DEC(cluster_number=10,
embedding_dimension=28 * 28,
hidden_dimension=10,
encoder=autoencoder.encoder)
model = model.to(device)
dec_optimizer = SGD(model.parameters(), lr=0.01, momentum=0.9)
train(dataset=ds_train,
model=model,
epochs=20000,
batch_size=256,
silent=True,
optimizer=dec_optimizer,
stopping_delta=0.000001,
cuda=cuda)
predicted, actual = predict(ds_train,
model,
1024,
silent=True,
return_actual=True,
cuda=cuda)
actual = actual.cpu().numpy()
predicted = predicted.cpu().numpy()
reassignment, accuracy = cluster_accuracy(actual, predicted)
print('Final DEC accuracy: %s' % accuracy)
if not testing_mode:
predicted_reassigned = [reassignment[item]
for item in predicted] # TODO numpify
confusion = confusion_matrix(actual, predicted_reassigned)
normalised_confusion = confusion.astype('float') / confusion.sum(
axis=1)[:, np.newaxis]
confusion_id = uuid.uuid4().hex
sns.heatmap(normalised_confusion).get_figure().savefig(
'confusion_%s.png' % confusion_id)
print('Writing out confusion diagram with UUID: %s' % confusion_id)
writer.close()
def setUpClass(cls):
cls.ae = StackedDenoisingAutoEncoder([100, 50, 5])
cls.dec = DEC(2, 100, 5, cls.ae.encoder)
def setUpClass(cls):
cls.dimensions = list(reversed(range(5, 11)))
cls.ae = StackedDenoisingAutoEncoder(cls.dimensions)
def main(cuda, batch_size, pretrain_epochs, finetune_epochs):
writer = SummaryWriter() # create the TensorBoard object
# callback function to call during training, uses writer from the scope
def training_callback(epoch, lr, loss, validation_loss):
writer.add_scalars('data/autoencoder', {
'lr': lr,
'loss': loss,
'validation_loss': validation_loss,
}, epoch)
ds_train = CachedMNIST(train=True, cuda=cuda) # training dataset
ds_val = CachedMNIST(train=False, cuda=cuda) # evaluation dataset
autoencoder = StackedDenoisingAutoEncoder([28 * 28, 500, 500, 2000, 10],
final_activation=None)
if cuda:
autoencoder.cuda()
print('Pretraining stage.')
ae.pretrain(
ds_train,
autoencoder,
cuda=cuda,
validation=ds_val,
epochs=pretrain_epochs,
batch_size=batch_size,
optimizer=lambda model: SGD(model.parameters(), lr=0.1, momentum=0.9),
scheduler=lambda x: StepLR(x, 100, gamma=0.1),
corruption=0.2)
print('Training stage.')
ae_optimizer = SGD(params=autoencoder.parameters(), lr=0.1, momentum=0.9)
ae.train(ds_train,
autoencoder,
cuda=cuda,
validation=ds_val,
epochs=finetune_epochs,
batch_size=batch_size,
optimizer=ae_optimizer,
scheduler=StepLR(ae_optimizer, 100, gamma=0.1),
corruption=0.2,
update_callback=training_callback)
print('k-Means stage')
dataloader = DataLoader(ds_train, batch_size=1024, shuffle=False)
kmeans = KMeans(n_clusters=10, n_init=20)
autoencoder.eval()
features = []
actual = []
for index, batch in enumerate(dataloader):
if (isinstance(batch, tuple)
or isinstance(batch, list)) and len(batch) == 2:
batch, value = batch # if we have a prediction label, separate it to actual
actual.append(value)
if cuda:
batch = batch.cuda(async=True)
batch = batch.squeeze(1).view(batch.size(0), -1)
features.append(autoencoder.encoder(batch).detach().cpu())
actual = torch.cat(actual).long().cpu().numpy()
predicted = kmeans.fit_predict(torch.cat(features).numpy())
reassignment, accuracy = cluster_accuracy(predicted, actual)
print('Final k-Means accuracy: %s' % accuracy)
predicted_reassigned = [reassignment[item]
for item in predicted] # TODO numpify
confusion = confusion_matrix(actual, predicted_reassigned)
normalised_confusion = confusion.astype('float') / confusion.sum(
axis=1)[:, np.newaxis]
confusion_id = uuid.uuid4().hex
sns.heatmap(normalised_confusion).get_figure().savefig('confusion_%s.png' %
confusion_id)
print('Writing out confusion diagram with UUID: %s' % confusion_id)
writer.add_embedding(
torch.cat(features),
metadata=predicted,
label_img=ds_train.ds.train_data.float().unsqueeze(1), # TODO bit ugly
tag='predicted')
writer.close()
class SDAETransformerBase(TransformerMixin, BaseEstimator):
def __init__(self,
dimensions: List[int],
cuda: Optional[bool] = None,
batch_size: int = 256,
pretrain_epochs: int = 200,
finetune_epochs: int = 500,
corruption: Optional[float] = 0.2,
optimiser_pretrain: Callable[[torch.nn.Module], torch.optim.Optimizer] = lambda x: SGD(x.parameters(), lr=0.1, momentum=0.9),
optimiser_train: Callable[[torch.nn.Module], torch.optim.Optimizer] = lambda x: SGD(x.parameters(), lr=0.1, momentum=0.9),
scheduler: Optional[Callable[[torch.optim.Optimizer], Any]] = lambda x: StepLR(x, 100, gamma=0.1),
final_activation: Optional[torch.nn.Module] = None) -> None:
self.cuda = torch.cuda.is_available() if cuda is None else cuda
self.batch_size = batch_size
self.dimensions = dimensions
self.pretrain_epochs = pretrain_epochs
self.finetune_epochs = finetune_epochs
self.optimiser_pretrain = optimiser_pretrain
self.optimiser_train = optimiser_train
self.scheduler = scheduler
self.corruption = corruption
self.autoencoder = None
self.final_activation = final_activation
def fit(self, X, y=None):
if issparse(X):
X = X.todense()
ds = TensorDataset(torch.from_numpy(X.astype(np.float32)))
self.autoencoder = StackedDenoisingAutoEncoder(self.dimensions, final_activation=self.final_activation)
if self.cuda:
self.autoencoder.cuda()
ae.pretrain(
ds,
self.autoencoder,
cuda=self.cuda,
epochs=self.pretrain_epochs,
batch_size=self.batch_size,
optimizer=self.optimiser_pretrain,
scheduler=self.scheduler,
corruption=0.2,
silent=True
)
ae_optimizer = self.optimiser_train(self.autoencoder)
ae.train(
ds,
self.autoencoder,
cuda=self.cuda,
epochs=self.finetune_epochs,
batch_size=self.batch_size,
optimizer=ae_optimizer,
scheduler=self.scheduler(ae_optimizer),
corruption=self.corruption,
silent=True
)
return self
def score(self, X, y=None, sample_weight=None) -> float:
loss_function = torch.nn.MSELoss()
if self.autoencoder is None:
raise NotFittedError
if issparse(X):
X = X.todense()
self.autoencoder.eval()
ds = TensorDataset(torch.from_numpy(X.astype(np.float32)))
dataloader = DataLoader(
ds,
batch_size=self.batch_size,
shuffle=False
)
loss = 0
for index, batch in enumerate(dataloader):
batch = batch[0]
if self.cuda:
batch = batch.cuda(non_blocking=True)
output = self.autoencoder(batch)
loss += float(loss_function(output, batch).item())
return loss
print('got dataset', flush=True)
ds_train.output = 2
# pretrain
pretrain_epochs = 300
finetune_epochs = 500
training_callback = None
cuda = torch.cuda.is_available()
ds_val = None
embedded_dim = get_embedded_dim()
try:
autoencoder = pickle.load(open(autoencoder_path, 'rb'))
except:
autoencoder = StackedDenoisingAutoEncoder(
dimensions=[embedded_dim, 500, 500, 2000, 10],
final_activation=None,
)
if cuda:
autoencoder.cuda()
print('SDAE Pretraining stage.', flush=True)
print(f'@ {time.time() - start_time}\n', flush=True)
ae.pretrain(
ds_train,
autoencoder,
cuda=cuda,
validation=ds_val,
epochs=pretrain_epochs,
batch_size=batch_size,
optimizer=lambda model: SGD(
model.parameters(), lr=0.1, momentum=0.9),
def get_opt(model, lr=args.pretrain_lr):
return torch.optim.SGD(params=model.parameters(), lr=lr, momentum=0.9)
def get_sched(opt):
return torch.optim.lr_scheduler.StepLR(optimizer=opt,
step_size=1,
gamma=args.lr_step,
last_epoch=-1)
print("Loading Data ...")
sys.stdout.flush()
dataset = get_dataset(args)
validation = None
ae = SDAE([dataset.dims] + args.layers)
timestamp = datetime.now().strftime('%Y-%m-%dT%H:%M:%S')
print("Pretraining ...")
sys.stdout.flush()
# pretrain
ptsdae.model.pretrain(dataset,
autoencoder=ae,
epochs=args.pretrain_epochs,
batch_size=args.batch_size,
optimizer=get_opt,
scheduler=get_sched,
validation=validation,
update_freq=args.pretrain_epochs // 50,
cuda=True,
from sklearn.cluster import DBSCAN
from sklearn.manifold import TSNE
from sc_dm.datasets import *
import torch
from ptsdae.sdae import StackedDenoisingAutoEncoder as SDAE
if __name__ == '__main__':
# #############################################################################
dset = sys.argv[1]
#raw_data = DuoBenchmark('data/datasets/'+dset+'.csv')
raw_data = FromPickle('data/embeddings/mouse-pca-15000-log1p-True.pickle')
model = SDAE([raw_data.dims, 7500, 500, 2000, 50])
#model.load_state_dict(torch.load('data/models/'+dset+'.pt'))
model.load_state_dict(torch.load(sys.argv[1]))
if int(torch.__version__.split('.')[1]) == 3:
var = torch.autograd.variable.Variable(torch.Tensor(raw_data.data))
else:
var = torch.Tensor(raw_data.data)
embedding = model.encoder(var).data.numpy()
labels = DBSCAN().fit(embedding).labels_
tsne_embedding = TSNE(n_components=2).fit_transform(embedding)
# #############################################################################
plt_file = 'data/plots/mouse_SDAE.pdf'
def main(cuda, batch_size, pretrain_epochs, finetune_epochs, testing_mode):
writer = SummaryWriter() # create the TensorBoard object
# callback function to call during training, uses writer from the scope
def training_callback(epoch, lr, loss, validation_loss):
writer.add_scalars(
"data/autoencoder",
{
"lr": lr,
"loss": loss,
"validation_loss": validation_loss,
},
epoch,
)
ds_train = CachedMNIST(train=True, cuda=cuda,
testing_mode=testing_mode) # training dataset
ds_val = CachedMNIST(train=False, cuda=cuda,
testing_mode=testing_mode) # evaluation dataset
autoencoder = StackedDenoisingAutoEncoder([28 * 28, 500, 500, 2000, 10],
final_activation=None)
if cuda:
autoencoder.cuda()
print("Pretraining stage.")
ae.pretrain(
ds_train,
autoencoder,
cuda=cuda,
validation=ds_val,
epochs=pretrain_epochs,
batch_size=batch_size,
optimizer=lambda model: SGD(model.parameters(), lr=0.1, momentum=0.9),
scheduler=lambda x: StepLR(x, 100, gamma=0.1),
corruption=0.2,
)
print("Training stage.")
ae_optimizer = SGD(params=autoencoder.parameters(), lr=0.1, momentum=0.9)
ae.train(
ds_train,
autoencoder,
cuda=cuda,
validation=ds_val,
epochs=finetune_epochs,
batch_size=batch_size,
optimizer=ae_optimizer,
scheduler=StepLR(ae_optimizer, 100, gamma=0.1),
corruption=0.2,
update_callback=training_callback,
)
print("DEC stage.")
model = DEC(cluster_number=10,
hidden_dimension=10,
encoder=autoencoder.encoder)
if cuda:
model.cuda()
dec_optimizer = SGD(model.parameters(), lr=0.01, momentum=0.9)
train(
dataset=ds_train,
model=model,
epochs=100,
batch_size=256,
optimizer=dec_optimizer,
stopping_delta=0.000001,
cuda=cuda,
)
predicted, actual = predict(ds_train,
model,
1024,
silent=True,
return_actual=True,
cuda=cuda)
actual = actual.cpu().numpy()
predicted = predicted.cpu().numpy()
reassignment, accuracy = cluster_accuracy(actual, predicted)
print("Final DEC accuracy: %s" % accuracy)
if not testing_mode:
predicted_reassigned = [reassignment[item]
for item in predicted] # TODO numpify
confusion = confusion_matrix(actual, predicted_reassigned)
normalised_confusion = (confusion.astype("float") /
confusion.sum(axis=1)[:, np.newaxis])
confusion_id = uuid.uuid4().hex
sns.heatmap(normalised_confusion).get_figure().savefig(
"confusion_%s.png" % confusion_id)
print("Writing out confusion diagram with UUID: %s" % confusion_id)
writer.close()
ds_path = os.path.join('data/datasets', ds_name + '.csv')
dataset = DuoBenchmark(ds_path, log1p=log, split_head=False)
for scale in [True]:
# Do scaling second as the function will
# overwrite the existing data
# yes - yes I know this is bad design but it's too late now
mlist = model_dict[ds_name][log][scale]
# Given all of the pre-existing conditions ...
# cycle through each of the models that match this criteria
for model in mlist:
filename = model[0]
print(filename)
if scale:
scale_dataset(dataset)
# get parameter information
model_path = os.path.join(model_dir, filename)
layers = model[1]
# prepare the model
model = SDAE([dataset.dims] + layers)
model.load_state_dict(
torch.load(model_path, map_location='cpu'))
# generate the embedding
inputs = torch.Tensor(dataset.data)
embedding = model.encoder(inputs).data.numpy()
# save the embedding
with open(
os.path.join('data/sdae_embeddings',
filename + '.pickle'), 'wb') as fh:
pickle.dump(embedding, fh, protocol=4)
