GDumb

class capymoa.ocl.strategy.GDumb

Bases: BatchClassifier, TestTaskAware

Greedy sampler and a dumb learner.

Greedy sampler and a dumb learner (GDumb) [1] is a baseline replay strategy. It works by down sampling the dataset and training offline. Since online learners do not have an inference time, GDumb is an offline algorithm, but GDumb remains a useful baseline.

[1]

Prabhu, A., Torr, P. H. S., & Dokania, P. K. (2020). GDumb: A Simple Approach that Questions Our Progress in Continual Learning. In A. Vedaldi, H. Bischof, T. Brox, & J.-M. Frahm (Eds.), Computer Vision – ECCV 2020 (pp. 524–540). Springer International Publishing.

__init__(

schema: Schema,

model: Module,

epochs: int,

batch_size: int,

capacity: int,

lr: float = 0.001,

device: str | device = 'cpu',

seed: int = 0,

)

batch_predict(x: Tensor) → Tensor

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

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

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,).

gdumb_fit() → None

Fit the model on the coreset.

on_test_task(task_id: int) → None

Called when testing on a task starts.

predict(instance: Instance) → int | None

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

Calls batch_predict_proba() with a batch of size 1.

train(instance: LabeledInstance) → None

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

Data type for the input features.

y_dtype: dtype = torch.int64

Data type for the target value/labels.