Dimensionality Reduction#

Algorithms for decomposition and dimensionality reduction.

class anomalib.models.components.dimensionality_reduction.PCA(n_components)#

Bases: DynamicBufferMixin

Principle Component Analysis (PCA).

Parameters:: n_components (float) – Number of components. Can be either integer number of components or a ratio between 0-1.

Example

>>> import torch
>>> from anomalib.models.components import PCA

Create a PCA model with 2 components:

>>> pca = PCA(n_components=2)

Create a random embedding and fit a PCA model.

>>> embedding = torch.rand(1000, 5).cuda()
>>> pca = PCA(n_components=2)
>>> pca.fit(embedding)

Apply transformation:

>>> transformed = pca.transform(embedding)
>>> transformed.shape
torch.Size([1000, 2])

fit(dataset)#

Fits the PCA model to the dataset.

Parameters:: dataset (torch.Tensor) – Input dataset to fit the model.
Return type:: None

Example

>>> pca.fit(embedding)
>>> pca.singular_vectors
tensor([9.6053, 9.2763], device='cuda:0')

>>> pca.mean
tensor([0.4859, 0.4959, 0.4906, 0.5010, 0.5042], device='cuda:0')

fit_transform(dataset)#

Fit and transform PCA to dataset.

Parameters:: dataset (torch.Tensor) – Dataset to which the PCA if fit and transformed
Return type:: Tensor
Returns:: Transformed dataset

Example

>>> pca.fit_transform(embedding)
>>> transformed_embedding = pca.fit_transform(embedding)
>>> transformed_embedding.shape
torch.Size([1000, 2])

forward(features)#

Transform the features.

Parameters:: features (torch.Tensor) – Input features
Return type:: Tensor
Returns:: Transformed features

Example

>>> pca(embedding).shape
torch.Size([1000, 2])

inverse_transform(features)#

Inverses the transformed features.

Parameters:: features (torch.Tensor) – Transformed features
Return type:: Tensor
Returns:: Inverse features

Example

>>> inverse_embedding = pca.inverse_transform(transformed_embedding)
>>> inverse_embedding.shape
torch.Size([1000, 5])

transform(features)#

Transform the features based on singular vectors calculated earlier.

Parameters:: features (torch.Tensor) – Input features
Return type:: Tensor
Returns:: Transformed features

Example

>>> pca.transform(embedding)
>>> transformed_embedding = pca.transform(embedding)

>>> embedding.shape
torch.Size([1000, 5])
#
>>> transformed_embedding.shape
torch.Size([1000, 2])

class anomalib.models.components.dimensionality_reduction.SparseRandomProjection(eps=0.1, random_state=None)#

Bases: object

Sparse Random Projection using PyTorch operations.

Parameters:

eps (float, optional) – Minimum distortion rate parameter for calculating Johnson-Lindenstrauss minimum dimensions. Defaults to 0.1.
random_state (int | None, optional) – Uses the seed to set the random state for sample_without_replacement function. Defaults to None.

Example

To fit and transform the embedding tensor, use the following code:

import torch
from anomalib.models.components import SparseRandomProjection

sparse_embedding = torch.rand(1000, 5).cuda()
model = SparseRandomProjection(eps=0.1)

Fit the model and transform the embedding tensor:

model.fit(sparse_embedding)
projected_embedding = model.transform(sparse_embedding)

print(projected_embedding.shape)
# Output: torch.Size([1000, 5920])

fit(embedding)#

Generate sparse matrix from the embedding tensor.

Parameters:

embedding (torch.Tensor) – embedding tensor for generating embedding

Returns:

Return self to be used as

>>> model = SparseRandomProjection()
>>> model = model.fit()

Return type:

(SparseRandomProjection)

transform(embedding)#

Project the data by using matrix product with the random matrix.

Parameters:

embedding (torch.Tensor) – Embedding of shape (n_samples, n_features) The input data to project into a smaller dimensional space

Returns:

Sparse matrix of shape: (n_samples, n_components) Projected array.

Return type:

projected_embedding (torch.Tensor)

Example

>>> projected_embedding = model.transform(embedding)
>>> projected_embedding.shape
torch.Size([1000, 5920])

Dimensionality Reduction

Contents

Dimensionality Reduction#