NCM#

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

Bases: BatchClassifier

Nearest Class Mean (NCM).

NCM is a simple classifier that uses the mean of each class as a prototype. It calculates the distance from each input to the class means and assigns the class with the closest mean as the predicted class.

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

Initialize a NCM classifier head.

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.

  • 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.