# `GDumb`

### *class* capymoa.ocl.strategy.GDumb[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/ocl/strategy/_gdumb.py#L12)

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

Greedy sampler and a dumb learner.

Greedy sampler and a dumb learner (GDumb) <sup>[1](#f0)</sup> 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.

* <a id='f0'>**[1]**</a> [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.](https://doi.org/10.1007/978-3-030-58536-5_31)

#### \_\_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), epochs: [int](https://docs.python.org/3/builtins/functions.html#int), batch_size: [int](https://docs.python.org/3/builtins/functions.html#int), capacity: [int](https://docs.python.org/3/builtins/functions.html#int), lr: [float](https://docs.python.org/3/builtins/functions.html#float) = 0.001, device: [str](https://docs.python.org/3/builtins/stdtypes.html#str) | [device](https://docs.pytorch.org/docs/stable/tensor_attributes.html#torch.device) = 'cpu', seed: [int](https://docs.python.org/3/builtins/functions.html#int) = 0)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/ocl/strategy/_gdumb.py#L27)

#### attach_with(source: [Dispatcher](capymoa.ocl.events.Dispatcher.md#capymoa.ocl.events.Dispatcher)) → [GDumb](#capymoa.ocl.strategy.GDumb)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/ocl/strategy/_gdumb.py#L92)

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.GDumb.x_dtype) valued feature vectors
  `(batch_size, num_features)`
* **Returns:**
  Predicted batch of [`y_dtype`](#capymoa.ocl.strategy.GDumb.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/_gdumb.py#L56)

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

* **Parameters:**
  **x** – Batch of [`x_dtype`](#capymoa.ocl.strategy.GDumb.x_dtype) valued feature vectors
  `(batch_size, num_features)`
* **Returns:**
  Batch of [`x_dtype`](#capymoa.ocl.strategy.GDumb.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/_gdumb.py#L53)

Train with a batch of instances.

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

#### gdumb_fit() → [None](https://docs.python.org/3/builtins/constants.html#None)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/ocl/strategy/_gdumb.py#L59)

Fit the model on the coreset.

#### 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(task_id: [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/ocl/strategy/_gdumb.py#L88)

#### 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.GDumb.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.GDumb.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.GDumb.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.

#### 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.