# `EWC`

### *class* capymoa.ocl.strategy.EWC[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/ocl/strategy/_ewc.py#L109)

Bases: [`BatchClassifier`](capymoa.base.BatchClassifier.md#capymoa.base.BatchClassifier), [`Module`](https://docs.pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module), [`Handler`](capymoa.ocl.events.Handler.md#capymoa.ocl.events.Handler)

Elastic Weight Consolidation learner.

Elastic Weight Consolidation (EWC) is a regularisation-based continual learning
strategy that mitigates catastrophic forgetting by penalising changes to important
parameters for previous tasks <sup>[1](#f1)</sup>. We incorporate Online EWC-style <sup>[2](#f2)</sup> updates
to the Fisher diagonals, which decay the importance of previous tasks’ parameters
over time based on the `gamma` hyperparameter.

Usually the EWC strategy has access to the entire active task’s data when estimating
the Fisher diagonals, but instead we use a replay buffer to approximate the active
task distribution.

* <a id='f1'>**[1]**</a> Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., Hassabis, D., Clopath, C., Kumaran, D., & Hadsell, R. (2017). Overcoming catastrophic forgetting in neural networks. Proceedings of the National Academy of Sciences, 114(13), 3521–3526. [https://doi.org/10.1073/pnas.1611835114](https://doi.org/10.1073/pnas.1611835114)
* <a id='f2'>**[2]**</a> Schwarz, J., Czarnecki, W., Luketina, J., Grabska-Barwinska, A., Teh, Y. W., Pascanu, R., & Hadsell, R. (2018). Progress & Compress: A scalable framework for continual learning. In J. G. Dy & A. Krause (Eds.), Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018 (Vol. 80, pp. 4535–4544). PMLR. [http://proceedings.mlr.press/v80/schwarz18a.html](http://proceedings.mlr.press/v80/schwarz18a.html)

#### \_\_init_\_(schema: [Schema](capymoa.stream.Schema.md#capymoa.stream.Schema), model: [Module](https://docs.pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module), optimiser: [Optimizer](https://docs.pytorch.org/docs/stable/optim.html#torch.optim.Optimizer), lambda_: [float](https://docs.python.org/3/builtins/functions.html#float), fim_buffer: [int](https://docs.python.org/3/builtins/functions.html#int) = 256, fim_batch_size: [int](https://docs.python.org/3/builtins/functions.html#int) = 32, device: [device](https://docs.pytorch.org/docs/stable/tensor_attributes.html#torch.device) | [None](https://docs.python.org/3/builtins/constants.html#None) = None, mask_test: [bool](https://docs.python.org/3/builtins/functions.html#bool) = False, mask_train: [bool](https://docs.python.org/3/builtins/functions.html#bool) = False, gamma: [float](https://docs.python.org/3/builtins/functions.html#float) = 1.0, task_mask: [Tensor](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor) | [None](https://docs.python.org/3/builtins/constants.html#None) = None) → [None](https://docs.python.org/3/builtins/constants.html#None)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/ocl/strategy/_ewc.py#L136)

Construct an EWC learner.

* **Parameters:**
  * **schema** – Stream schema used by the classifier interface.
  * **model** – Torch model that outputs class logits.
  * **optimiser** – Optimiser used to update `model` parameters.
  * **lambda** – Weight of the EWC regularisation term.
  * **fim_buffer** – Replay window size for Fisher estimation.
  * **fim_batch_size** – Mini-batch size used when estimating Fisher diagonals.
  * **device** – Compute device.
  * **mask_test** – Whether to apply per-task masking during testing. This is a
    task incremental scenario.
  * **mask_train** – Whether to apply per-task masking during training. This is
    also known as the labels trick.
  * **task_mask** – Optional per-task mask applied to output logits.
* **Raises:**
  [**ValueError**](https://docs.python.org/3/builtins/exceptions.html#ValueError) – If task-specific masking is requested without `task_mask`.

#### attach_with(source: [Dispatcher](capymoa.ocl.events.Dispatcher.md#capymoa.ocl.events.Dispatcher)) → [EWC](#capymoa.ocl.strategy.EWC)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/ocl/strategy/_ewc.py#L224)

Attach this sink to an event source.

Implementations should call
[`capymoa.ocl.events.Dispatcher.subscribe()`](capymoa.ocl.events.Dispatcher.md#capymoa.ocl.events.Dispatcher.subscribe) for each
event type the sink needs to handle.

* **Parameters:**
  **dispatcher** – The source this sink should subscribe to.

#### batch_predict(x: [Tensor](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor)) → [Tensor](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/base/_batch_classifier.py#L115)

Predict the labels for a batch of instances.

* **Parameters:**
  **x** – Batch of [`x_dtype`](#capymoa.ocl.strategy.EWC.x_dtype) valued feature vectors
  `(batch_size, num_features)`
* **Returns:**
  Predicted batch of [`y_dtype`](#capymoa.ocl.strategy.EWC.y_dtype) valued labels
  `(batch_size,)`.

#### batch_predict_proba(x: [Tensor](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor)) → [Tensor](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/ocl/strategy/_ewc.py#L218)

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

* **Parameters:**
  **x** – Batch of [`x_dtype`](#capymoa.ocl.strategy.EWC.x_dtype) valued feature vectors
  `(batch_size, num_features)`
* **Returns:**
  Batch of [`x_dtype`](#capymoa.ocl.strategy.EWC.x_dtype) valued predicted probabilities
  `(batch_size, num_classes)`.

#### batch_train(x: [Tensor](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor), y: [Tensor](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor)) → [None](https://docs.python.org/3/builtins/constants.html#None)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/ocl/strategy/_ewc.py#L208)

Train with a batch of instances.

* **Parameters:**
  * **x** – Batch of [`x_dtype`](#capymoa.ocl.strategy.EWC.x_dtype) valued feature vectors
    `(batch_size, num_features)`
  * **y** – Batch of [`y_dtype`](#capymoa.ocl.strategy.EWC.y_dtype) valued labels `(batch_size,)`.

#### *classmethod* from_params(schema: [Any](https://docs.python.org/3/library/typing.html#typing.Any) = None, params: [dict](https://docs.python.org/3/builtins/stdtypes.html#dict)[[str](https://docs.python.org/3/builtins/stdtypes.html#str), [Any](https://docs.python.org/3/library/typing.html#typing.Any)] | [None](https://docs.python.org/3/builtins/constants.html#None) = None, random_seed: [int](https://docs.python.org/3/builtins/functions.html#int) = 1) → [Any](https://docs.python.org/3/library/typing.html#typing.Any)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/base/_learner_params.py#L170)

Construct an instance from parameters produced by `get_params`.

#### get_params() → [dict](https://docs.python.org/3/builtins/stdtypes.html#dict)[[str](https://docs.python.org/3/builtins/stdtypes.html#str), [Any](https://docs.python.org/3/library/typing.html#typing.Any)][[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/base/_learner_params.py#L163)

Return the hyper-parameters captured from the constructor.

#### on_test_task(event: [TestTaskBegin](capymoa.ocl.evaluation.events.TestTaskBegin.md#capymoa.ocl.evaluation.events.TestTaskBegin)) → [None](https://docs.python.org/3/builtins/constants.html#None)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/ocl/strategy/_ewc.py#L235)

#### on_train_task(event: [TrainTaskBegin](capymoa.ocl.evaluation.events.TrainTaskBegin.md#capymoa.ocl.evaluation.events.TrainTaskBegin)) → [None](https://docs.python.org/3/builtins/constants.html#None)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/ocl/strategy/_ewc.py#L229)

#### predict(instance: [Instance](capymoa.core.Instance.md#capymoa.core.Instance)) → [int](https://docs.python.org/3/builtins/functions.html#int) | [None](https://docs.python.org/3/builtins/constants.html#None)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/base/_classifier.py#L56)

Predict the label of an instance.

The base implementation calls [`predict_proba()`](#capymoa.ocl.strategy.EWC.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](capymoa.core.Instance.md#capymoa.core.Instance)) → [ndarray](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)[[tuple](https://docs.python.org/3/builtins/stdtypes.html#tuple)[[Any](https://docs.python.org/3/library/typing.html#typing.Any), ...], [dtype](https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype)[float64]] | [None](https://docs.python.org/3/builtins/constants.html#None)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/base/_batch_classifier.py#L134)

Calls [`batch_predict_proba()`](#capymoa.ocl.strategy.EWC.batch_predict_proba) with a batch of size 1.

#### train(instance: [LabeledInstance](capymoa.core.LabeledInstance.md#capymoa.core.LabeledInstance)) → [None](https://docs.python.org/3/builtins/constants.html#None)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/base/_batch_classifier.py#L125)

Calls [`batch_train()`](#capymoa.ocl.strategy.EWC.batch_train) with a batch of size 1.

#### T_destination *= ~T_destination*

#### call_super_init *: [bool](https://docs.python.org/3/builtins/functions.html#bool)* *= False*

#### device *: [torch.device](https://docs.pytorch.org/docs/stable/tensor_attributes.html#torch.device)* *= device(type='cpu')*

Device on which the batch will be processed.

#### dump_patches *: [bool](https://docs.python.org/3/builtins/functions.html#bool)* *= False*

#### random_seed *: [int](https://docs.python.org/3/builtins/functions.html#int)*

The random seed for reproducibility.

When implementing a classifier ensure random number generators are seeded.

#### schema *: [Schema](capymoa.stream.Schema.md#capymoa.stream.Schema)*

The schema representing the instances.

#### training *: [bool](https://docs.python.org/3/builtins/functions.html#bool)*

#### x_dtype *: [torch.dtype](https://docs.pytorch.org/docs/stable/tensor_attributes.html#torch.dtype)* *= torch.float32*

Data type for the input features.

#### y_dtype *: [torch.dtype](https://docs.pytorch.org/docs/stable/tensor_attributes.html#torch.dtype)* *= torch.int64*

Data type for the target value/labels.