"""
@authors:
Lukas Miklautz,
Dominik Mautz
"""
from clustpy.deep._utils import detect_device, encode_batchwise, squared_euclidean_distance, predict_batchwise, \
set_torch_seed, run_initial_clustering, int_to_one_hot, embedded_kmeans_prediction
from clustpy.deep._data_utils import get_dataloader, augmentation_invariance_check
from clustpy.deep._train_utils import get_trained_autoencoder
import torch
import numpy as np
from sklearn.cluster import KMeans
from sklearn.base import BaseEstimator, ClusterMixin
from sklearn.utils import check_random_state
def _dcn(X: np.ndarray, n_clusters: int, batch_size: int, pretrain_optimizer_params: dict,
clustering_optimizer_params: dict, pretrain_epochs: int, clustering_epochs: int,
optimizer_class: torch.optim.Optimizer, loss_fn: torch.nn.modules.loss._Loss, autoencoder: torch.nn.Module,
embedding_size: int, degree_of_space_distortion: float, degree_of_space_preservation: float,
custom_dataloaders: tuple, augmentation_invariance: bool, initial_clustering_class: ClusterMixin,
initial_clustering_params: dict,
random_state: np.random.RandomState) -> (np.ndarray, np.ndarray, np.ndarray, np.ndarray, torch.nn.Module):
"""
Start the actual DCN clustering procedure on the input data set.
Parameters
----------
X : np.ndarray / torch.Tensor
the given data set. Can be a np.ndarray or a torch.Tensor
n_clusters : int
number of clusters. Can be None if a corresponding initial_clustering_class is given, e.g. DBSCAN
batch_size : int
size of the data batches
pretrain_optimizer_params : dict
parameters of the optimizer for the pretraining of the autoencoder, includes the learning rate
clustering_optimizer_params : dict
parameters of the optimizer for the actual clustering procedure, includes the learning rate
pretrain_epochs : int
number of epochs for the pretraining of the autoencoder
clustering_epochs : int
number of epochs for the actual clustering procedure
optimizer_class : torch.optim.Optimizer
the optimizer class
loss_fn : torch.nn.modules.loss._Loss
loss function for the reconstruction
autoencoder : torch.nn.Module
the input autoencoder. If None a new FeedforwardAutoencoder will be created
embedding_size : int
size of the embedding within the autoencoder
degree_of_space_distortion : float
weight of the reconstruction loss
degree_of_space_preservation : float
weight of the clustering loss
custom_dataloaders : tuple
tuple consisting of a trainloader (random order) at the first and a test loader (non-random order) at the second position.
If None, the default dataloaders will be used
augmentation_invariance : bool
If True, augmented samples provided in custom_dataloaders[0] will be used to learn
cluster assignments that are invariant to the augmentation transformations
initial_clustering_class : ClusterMixin
clustering class to obtain the initial cluster labels after the pretraining
initial_clustering_params : dict
parameters for the initial clustering class
random_state : np.random.RandomState
use a fixed random state to get a repeatable solution
Returns
-------
tuple : (np.ndarray, np.ndarray, np.ndarray, np.ndarray, torch.nn.Module)
The labels as identified by a final KMeans execution,
The cluster centers as identified by a final KMeans execution,
The labels as identified by DCN after the training terminated,
The cluster centers as identified by DCN after the training terminated,
The final autoencoder
"""
device = detect_device()
if custom_dataloaders is None:
trainloader = get_dataloader(X, batch_size, True, False)
testloader = get_dataloader(X, batch_size, False, False)
else:
trainloader, testloader = custom_dataloaders
autoencoder = get_trained_autoencoder(trainloader, pretrain_optimizer_params, pretrain_epochs, device,
optimizer_class, loss_fn, embedding_size, autoencoder)
# Execute initial clustering in embedded space
embedded_data = encode_batchwise(testloader, autoencoder, device)
n_clusters, _, init_centers, _ = run_initial_clustering(embedded_data, n_clusters,
initial_clustering_class,
initial_clustering_params, random_state)
# Setup DCN Module
dcn_module = _DCN_Module(init_centers, augmentation_invariance).to_device(device)
# Use DCN optimizer parameters (usually learning rate is reduced by a magnitude of 10)
optimizer = optimizer_class(list(autoencoder.parameters()), **clustering_optimizer_params)
# DEC Training loop
dcn_module.fit(autoencoder, trainloader, clustering_epochs, device, optimizer, loss_fn,
degree_of_space_distortion, degree_of_space_preservation)
# Get labels
dcn_labels = predict_batchwise(testloader, autoencoder, dcn_module, device)
dcn_centers = dcn_module.centers.detach().cpu().numpy()
# Do reclustering with Kmeans
embedded_data = encode_batchwise(testloader, autoencoder, device)
kmeans = KMeans(n_clusters=n_clusters, random_state=random_state)
kmeans.fit(embedded_data)
return kmeans.labels_, kmeans.cluster_centers_, dcn_labels, dcn_centers, autoencoder
def _compute_centroids(centers: torch.Tensor, embedded: torch.Tensor, count: torch.Tensor, labels: torch.Tensor) -> (
torch.Tensor, torch.Tensor):
"""
Update the centers and amount of object ever assigned to a center.
New center is calculated by (see Eq. 8 in the paper):
center - eta (center - embedded[i])
=> center - eta * center + eta * embedded[i]
=> (1 - eta) center + eta * embedded[i]
Parameters
----------
centers : torch.Tensor
The current cluster centers
embedded : torch.Tensor
The embedded samples
count : torch.Tensor
The total amount of objects that ever got assigned to a cluster. Affects the learning rate of the center update
labels : torch.Tensor
The current hard labels
Returns
-------
centers, count : (torch.Tensor, torch.Tensor)
The updated centers and the updated counts
"""
for i in range(embedded.shape[0]):
c = labels[i].item()
count[c] += 1
eta = 1.0 / count[c].item()
centers[c] = (1 - eta) * centers[c] + eta * embedded[i]
return centers, count
class _DCN_Module(torch.nn.Module):
"""
The _DCN_Module. Contains most of the algorithm specific procedures like the loss and prediction functions.
Parameters
----------
init_np_centers : np.ndarray
The initial numpy centers
augmentation_invariance : bool
If True, augmented samples provided in will be used to learn
cluster assignments that are invariant to the augmentation transformations (default: False)
Attributes
----------
centers : torch.Tensor
the cluster centers
augmentation_invariance : bool
Is augmentation invariance used
"""
def __init__(self, init_np_centers: np.ndarray, augmentation_invariance: bool = False):
super().__init__()
self.augmentation_invariance = augmentation_invariance
self.centers = torch.tensor(init_np_centers)
def dcn_loss(self, embedded: torch.Tensor, assignment_matrix: torch.Tensor = None,
weights: torch.Tensor = None) -> torch.Tensor:
"""
Calculate the DCN loss of given embedded samples.
Parameters
----------
embedded : torch.Tensor
the embedded samples
assignment_matrix : torch.Tensor
cluster assignments per sample as a one-hot-matrix to compute the loss.
If None then loss will be computed based on the closest centroids for each data sample (default: None)
weights : torch.Tensor
feature weights for the squared euclidean distance (default: None)
Returns
-------
loss: torch.Tensor
the final DCN loss
"""
dist = squared_euclidean_distance(embedded, self.centers, weights=weights)
if assignment_matrix is None:
loss = (dist.min(dim=1)[0]).mean()
else:
loss = (dist * assignment_matrix).mean()
return loss
def predict_hard(self, embedded: torch.Tensor, weights: torch.Tensor = None) -> torch.Tensor:
"""
Hard prediction of the given embedded samples. Returns the corresponding hard labels.
Uses the minimum squared euclidean distance to the cluster centers to get the labels.
Parameters
----------
embedded : torch.Tensor
the embedded samples
weights : torch.Tensor
feature weights for the squared euclidean distance (default: None)
Returns
-------
labels : torch.Tensor
the final labels
"""
dist = squared_euclidean_distance(embedded, self.centers, weights=weights)
labels = (dist.min(dim=1)[1])
return labels
def update_centroids(self, embedded: torch.Tensor, count: torch.Tensor, labels: torch.Tensor) -> torch.Tensor:
"""
Update the cluster centers of the _DCN_Module.
Parameters
----------
embedded : torch.Tensor
the embedded samples
count : torch.Tensor
The total amount of objects that ever got assigned to a cluster. Affects the learning rate of the center update
labels : torch.Tensor
The current hard labels
Returns
-------
count : torch.Tensor
The new amount of objects that ever got assigned to a cluster
"""
self.centers, count = _compute_centroids(self.centers, embedded, count, labels)
return count
def to_device(self, device: torch.device) -> '_DCN_Module':
"""
Move the _DCN_Module and the cluster centers to the specified device (cpu or cuda).
Parameters
----------
device : torch.device
device to be trained on
Returns
-------
self : _DCN_Module
this instance of the _DCN_Module
"""
self.centers = self.centers.to(device)
self.to(device)
return self
def _loss(self, batch: list, autoencoder: torch.nn.Module, loss_fn: torch.nn.modules.loss._Loss,
degree_of_space_preservation: float, degree_of_space_distortion: float, device: torch.device):
"""
Calculate the complete DCN + Autoencoder loss.
Parameters
----------
batch : list
the minibatch
autoencoder : torch.nn.Module
the autoencoder
loss_fn : torch.nn.modules.loss._Loss
loss function for the reconstruction
degree_of_space_distortion : float
weight of the clustering loss
degree_of_space_preservation : float
weight of the reconstruction loss
device : torch.device
device to be trained on
Returns
-------
loss : torch.Tensor
the final DCN loss
"""
# compute reconstruction loss
if self.augmentation_invariance:
# Convention is that the augmented sample is at the first position and the original one at the second position
ae_loss, embedded, _ = autoencoder.loss([batch[0], batch[2]], loss_fn, device)
ae_loss_aug, embedded_aug, _ = autoencoder.loss([batch[0], batch[1]], loss_fn, device)
ae_loss = (ae_loss + ae_loss_aug) / 2
else:
ae_loss, embedded, _ = autoencoder.loss(batch, loss_fn, device)
# compute cluster loss
assignments = self.predict_hard(embedded)
assignment_matrix = int_to_one_hot(assignments, self.centers.shape[0])
cluster_loss = self.dcn_loss(embedded, assignment_matrix)
if self.augmentation_invariance:
# assign augmented samples to the same cluster as original samples
cluster_loss_aug = self.dcn_loss(embedded_aug, assignment_matrix)
cluster_loss = (cluster_loss + cluster_loss_aug) / 2
# compute total loss
loss = degree_of_space_preservation * ae_loss + 0.5 * degree_of_space_distortion * cluster_loss
return loss
def fit(self, autoencoder: torch.nn.Module, trainloader: torch.utils.data.DataLoader, n_epochs: int,
device: torch.device, optimizer: torch.optim.Optimizer, loss_fn: torch.nn.modules.loss._Loss,
degree_of_space_distortion: float, degree_of_space_preservation: float) -> '_DCN_Module':
"""
Trains the _DCN_Module in place.
Parameters
----------
autoencoder : torch.nn.Module
the autoencoder
trainloader : torch.utils.data.DataLoader
dataloader to be used for training
n_epochs : int
number of epochs for the clustering procedure
device : torch.device
device to be trained on
optimizer : torch.optim.Optimizer
the optimizer for training
loss_fn : torch.nn.modules.loss._Loss
loss function for the reconstruction
degree_of_space_distortion : float
weight of the clustering loss
degree_of_space_preservation : float
weight of the reconstruction loss
Returns
-------
self : _DCN_Module
this instance of the _DCN_Module
"""
# DCN training loop
# Init for count from original DCN code (not reported in Paper)
# This means centroid learning rate at the beginning is scaled by a hundred
count = torch.ones(self.centers.shape[0], dtype=torch.int32) * 100
for _ in range(n_epochs):
# Update Network
for batch in trainloader:
loss = self._loss(batch, autoencoder, loss_fn, degree_of_space_preservation, degree_of_space_distortion,
device)
# Backward pass - update weights
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Update Assignments and Centroids
with torch.no_grad():
for batch in trainloader:
if self.augmentation_invariance:
# Convention is that the augmented sample is at the first position and the original one at the second position
# We only use the original sample for updating the centroids and assignments
batch_data = batch[2].to(device)
else:
batch_data = batch[1].to(device)
embedded = autoencoder.encode(batch_data)
## update centroids [on gpu] About 40 seconds for 1000 iterations
## No overhead from loading between gpu and cpu
# count = cluster_module.update_centroid(embedded, count, s)
# update centroids [on cpu] About 30 Seconds for 1000 iterations
# with additional overhead from loading between gpu and cpu
embedded = embedded.cpu()
self.centers = self.centers.cpu()
# update assignments
labels = self.predict_hard(embedded)
# update centroids
count = self.update_centroids(embedded, count.cpu(), labels.cpu())
# count = count.to(device)
self.centers = self.centers.to(device)
return self
[docs]class DCN(BaseEstimator, ClusterMixin):
"""
The Deep Clustering Network (DCN) algorithm.
First, an autoencoder (AE) will be trained (will be skipped if input autoencoder is given).
Afterward, KMeans identifies the initial clusters.
Last, the AE will be optimized using the DCN loss function.
Parameters
----------
n_clusters : int
number of clusters. Can be None if a corresponding initial_clustering_class is given, e.g. DBSCAN
batch_size : int
size of the data batches (default: 256)
pretrain_optimizer_params : dict
parameters of the optimizer for the pretraining of the autoencoder, includes the learning rate (default: {"lr": 1e-3})
clustering_optimizer_params : dict
parameters of the optimizer for the actual clustering procedure, includes the learning rate (default: {"lr": 1e-4})
pretrain_epochs : int
number of epochs for the pretraining of the autoencoder (default: 100)
clustering_epochs : int
number of epochs for the actual clustering procedure (default: 150)
optimizer_class : torch.optim.Optimizer
the optimizer class (default: torch.optim.Adam)
loss_fn : torch.nn.modules.loss._Loss
loss function for the reconstruction (default: torch.nn.MSELoss())
degree_of_space_distortion : float
weight of the clustering loss (default: 0.05)
degree_of_space_preservation : float
weight of the reconstruction loss (default: 1.0)
autoencoder : torch.nn.Module
the input autoencoder. If None a new FeedforwardAutoencoder will be created (default: None)
embedding_size : int
size of the embedding within the autoencoder (default: 10)
custom_dataloaders : tuple
tuple consisting of a trainloader (random order) at the first and a test loader (non-random order) at the second position.
If None, the default dataloaders will be used (default: None)
augmentation_invariance : bool
If True, augmented samples provided in custom_dataloaders[0] will be used to learn
cluster assignments that are invariant to the augmentation transformations (default: False)
initial_clustering_class : ClusterMixin
clustering class to obtain the initial cluster labels after the pretraining (default: KMeans)
initial_clustering_params : dict
parameters for the initial clustering class (default: {})
random_state : np.random.RandomState
use a fixed random state to get a repeatable solution. Can also be of type int (default: None)
Attributes
----------
labels_ : np.ndarray
The final labels (obtained by a final KMeans execution)
cluster_centers_ : np.ndarray
The final cluster centers (obtained by a final KMeans execution)
dcn_labels_ : np.ndarray
The final DCN labels
dcn_cluster_centers_ : np.ndarray
The final DCN cluster centers
autoencoder : torch.nn.Module
The final autoencoder
Examples
----------
>>> from clustpy.data import create_subspace_data
>>> from clustpy.deep import DCN
>>> data, labels = create_subspace_data(1500, subspace_features=(3, 50), random_state=1)
>>> dcn = DCN(n_clusters=3, pretrain_epochs=3, clustering_epochs=3)
>>> dcn.fit(data)
References
----------
Yang, Bo, et al. "Towards k-means-friendly spaces:
Simultaneous deep learning and clustering." international
conference on machine learning. PMLR, 2017.
"""
def __init__(self, n_clusters: int, batch_size: int = 256, pretrain_optimizer_params: dict = {"lr": 1e-3},
clustering_optimizer_params: dict = {"lr": 1e-4}, pretrain_epochs: int = 100,
clustering_epochs: int = 150, optimizer_class: torch.optim.Optimizer = torch.optim.Adam,
loss_fn: torch.nn.modules.loss._Loss = torch.nn.MSELoss(), degree_of_space_distortion: float = 0.05,
degree_of_space_preservation: float = 1.0, autoencoder: torch.nn.Module = None,
embedding_size: int = 10, custom_dataloaders: tuple = None, augmentation_invariance: bool = False,
initial_clustering_class: ClusterMixin = KMeans, initial_clustering_params: dict = {},
random_state: np.random.RandomState = None):
self.n_clusters = n_clusters
self.batch_size = batch_size
self.pretrain_optimizer_params = pretrain_optimizer_params
self.clustering_optimizer_params = clustering_optimizer_params
self.pretrain_epochs = pretrain_epochs
self.clustering_epochs = clustering_epochs
self.optimizer_class = optimizer_class
self.loss_fn = loss_fn
self.degree_of_space_distortion = degree_of_space_distortion
self.degree_of_space_preservation = degree_of_space_preservation
self.autoencoder = autoencoder
self.embedding_size = embedding_size
self.custom_dataloaders = custom_dataloaders
self.augmentation_invariance = augmentation_invariance
self.initial_clustering_class = initial_clustering_class
self.initial_clustering_params = initial_clustering_params
self.random_state = check_random_state(random_state)
set_torch_seed(self.random_state)
[docs] def fit(self, X: np.ndarray, y: np.ndarray = None) -> 'DCN':
"""
Initiate the actual clustering process on the input data set.
The resulting cluster labels will be stored in the labels_ attribute.
Parameters
----------
X : np.ndarray
the given data set
y : np.ndarray
the labels (can be ignored)
Returns
-------
self : DCN
this instance of the DCN algorithm
"""
augmentation_invariance_check(self.augmentation_invariance, self.custom_dataloaders)
kmeans_labels, kmeans_centers, dcn_labels, dcn_centers, autoencoder = _dcn(X, self.n_clusters, self.batch_size,
self.pretrain_optimizer_params,
self.clustering_optimizer_params,
self.pretrain_epochs,
self.clustering_epochs,
self.optimizer_class, self.loss_fn,
self.autoencoder,
self.embedding_size,
self.degree_of_space_distortion,
self.degree_of_space_preservation,
self.custom_dataloaders,
self.augmentation_invariance,
self.initial_clustering_class,
self.initial_clustering_params,
self.random_state)
self.labels_ = kmeans_labels
self.cluster_centers_ = kmeans_centers
self.dcn_labels_ = dcn_labels
self.dcn_cluster_centers_ = dcn_centers
self.autoencoder = autoencoder
return self
[docs] def predict(self, X: np.ndarray) -> np.ndarray:
"""
Predicts the labels of the input data.
Parameters
----------
X : np.ndarray
input data
Returns
-------
predicted_labels : np.ndarray
The predicted labels
"""
dataloader = get_dataloader(X, self.batch_size, False, False)
predicted_labels = embedded_kmeans_prediction(dataloader, self.cluster_centers_, self.autoencoder)
return predicted_labels