# `ExperienceReplay`

### *class* capymoa.ocl.strategy.ExperienceReplay[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/ocl/strategy/_experience_replay.py#L9)

Bases: [`BatchClassifier`](capymoa.base.BatchClassifier.md#capymoa.base.BatchClassifier), [`Handler`](capymoa.ocl.events.Handler.md#capymoa.ocl.events.Handler)

Experience Replay.

Experience Replay (ER) <sup>[1](#f0)</sup> is a replay continual learning strategy.

* Uses a replay buffer to store past experiences and samples from it during training
  to mitigate catastrophic forgetting.
* The replay buffer is implemented using reservoir sampling, which allows for
  uniform sampling over the entire stream <sup>[2](#f1)</sup>.

```pycon
>>> from capymoa.core.torch.ann import Perceptron
>>> from capymoa.classifier import Finetune
>>> from capymoa.ocl.strategy import ExperienceReplay
>>> from capymoa.ocl.datasets import TinySplitMNIST
>>> from capymoa.ocl.evaluation import ocl_train_eval_loop
>>> import torch
>>> _ = torch.manual_seed(0)
>>> scenario = TinySplitMNIST()
>>> model = Perceptron(scenario.schema)
>>> learner = ExperienceReplay(Finetune(scenario.schema, model))
>>> results = ocl_train_eval_loop(
...     learner,
...     scenario.train_loaders(32),
...     scenario.test_loaders(32),
... )
>>> print(f"{results.accuracy_final*100:.1f}%")
28.5%
```

* <a id='f0'>**[1]**</a> [Rolnick, D., Ahuja, A., Schwarz, J., Lillicrap, T., & Wayne, G. (2019). Experience replay for continual learning. Advances in neural information processing systems, 32.](https://arxiv.org/abs/1811.11682)
* <a id='f1'>**[2]**</a> [Jeffrey S. Vitter. 1985. Random sampling with a reservoir. ACM Trans. Math. Softw. 11, 1 (March 1985), 37–57.](https://doi.org/10.1145/3147.3165)

#### \_\_init_\_(learner: [BatchClassifier](capymoa.base.BatchClassifier.md#capymoa.base.BatchClassifier), buffer_size: [int](https://docs.python.org/3/builtins/functions.html#int) = 200, repeat: [int](https://docs.python.org/3/builtins/functions.html#int) = 1) → [None](https://docs.python.org/3/builtins/constants.html#None)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/ocl/strategy/_experience_replay.py#L44)

Initialize the Experience Replay strategy.

* **Parameters:**
  * **learner** – The learner to be wrapped for experience replay.
  * **buffer_size** – The size of the replay buffer, defaults to 200.
  * **repeat** – The number of times to repeat the training data in each batch,
    defaults to 1.

#### attach_with(source: [Dispatcher](capymoa.ocl.events.Dispatcher.md#capymoa.ocl.events.Dispatcher)) → [ExperienceReplay](#capymoa.ocl.strategy.ExperienceReplay)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/ocl/strategy/_experience_replay.py#L81)

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.ExperienceReplay.x_dtype) valued feature vectors
  `(batch_size, num_features)`
* **Returns:**
  Predicted batch of [`y_dtype`](#capymoa.ocl.strategy.ExperienceReplay.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/_experience_replay.py#L77)

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

* **Parameters:**
  **x** – Batch of [`x_dtype`](#capymoa.ocl.strategy.ExperienceReplay.x_dtype) valued feature vectors
  `(batch_size, num_features)`
* **Returns:**
  Batch of [`x_dtype`](#capymoa.ocl.strategy.ExperienceReplay.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/_experience_replay.py#L64)

Train with a batch of instances.

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

#### 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.ExperienceReplay.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.ExperienceReplay.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.ExperienceReplay.batch_train) with a batch of size 1.

#### 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.

#### learner

The wrapped learner to be trained with experience replay.

#### 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.

#### 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.