SLDA#

class capymoa.ocl.strategy.SLDA[source]#

Bases: BatchClassifier

Streaming Linear Discriminant Analysis (SLDA).

SLDA incrementally accumulates the mean of each class and a joint mean and covariance for all classes. Note that this method does not gracefully forget and may not handle concept drift well.

[Hayes20] uses SLDA ontop of a pre-trained model to perform continual learning. See [WikipediaILDA] and [Ghassabeh2015] for more details on incremental LDA outside of continual learning.

[Hayes20]

Hayes, T. L., & Kanan, C. (2020). Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis. CLVision Workshop at CVPR 2020, 1–15.

[Ghassabeh2015]

Ghassabeh, Y. A., Rudzicz, F., & Moghaddam, H. A. (2015). Fast incremental LDA feature extraction. Pattern Recognition, 48(6), 1999-2012.

__init__(
schema: Schema,
pre_processor: Module = nn.Identity(),
num_features: int | None = None,
ridge: float = 1e-6,
device: device | str = torch.device('cpu'),
)[source]#

Initialize a SLDA classifier.

Parameters:

  • schema – Describes the shape and type of the data.

  • pre_processor – A pre-processing module to apply to the input data, defaults to an identity module.

  • num_features – Number of features once pre-processed, defaults to the number of attributes in the schema.

  • ridge – Ridge regularization term to avoid singular covariance matrix, defaults to 1e-6.

  • device – Device to run the model on, defaults to CPU.

batch_predict(x: Tensor) → Tensor[source]#

Predict the labels for a batch of instances.

Parameters:

x – Batch of x_dtype valued feature vectors (batch_size, num_features)

Returns:

Predicted batch of y_dtype valued labels (batch_size,).

batch_predict_proba(x: Tensor) → Tensor[source]#

Predict the probabilities of the classes for a batch of instances.

Parameters:

x – Batch of x_dtype valued feature vectors (batch_size, num_features)

Returns:

Batch of x_dtype valued predicted probabilities (batch_size, num_classes).

batch_train(x: Tensor, y: Tensor) → None[source]#

Train with a batch of instances.

Parameters:

  • x – Batch of x_dtype valued feature vectors (batch_size, num_features)

  • y – Batch of y_dtype valued labels (batch_size,).

predict(instance: Instance) → int | None[source]#

Predict the label of an instance.

The base implementation calls predict_proba() and returns the label with the highest probability.

Parameters:

instance – The instance to predict the label for.

Returns:

The predicted label or None if the classifier is unable to make a prediction.

predict_proba(
instance: Instance,
) → ndarray[Any, dtype[float64]] | None[source]#

Calls batch_predict_proba() with a batch of size 1.

train(instance: LabeledInstance) → None[source]#

Calls batch_train() with a batch of size 1.

device: device = device(type='cpu')#

Device on which the batch will be processed.

random_seed: int#

The random seed for reproducibility.

When implementing a classifier ensure random number generators are seeded.

schema: Schema#

The schema representing the instances.

x_dtype: dtype = torch.float32[source]#

Data type for the input features.

y_dtype: dtype = torch.int64[source]#

Data type for the target value/labels.