SGDRegressor

class capymoa.regressor.SGDRegressor

Bases: SKRegressor

Streaming stochastic gradient descent regressor.

This wraps sklearn.linear_model.SGDRegressor for ease of use in the streaming context. Some options are missing because they are not relevant in the streaming context. Furthermore, the learning rate is constant.

Example Usage:

>>> from capymoa.datasets import Fried
>>> from capymoa.regressor import PassiveAggressiveRegressor
>>> from capymoa.evaluation import prequential_evaluation
>>> stream = Fried()
>>> schema = stream.get_schema()
>>> learner = SGDRegressor(schema)
>>> results = prequential_evaluation(stream, learner, max_instances=1000)
>>> results["cumulative"].rmse()
4.625212156832521

sklearner: SGDRegressor

The underlying scikit-learn object

__init__(

schema: Schema,

loss: Literal['squared_error', 'huber', 'epsilon_insensitive', 'squared_epsilon_insensitive'] = 'squared_error',

penalty: Literal['l2', 'l1', 'elasticnet'] | None = 'l2',

alpha: float = 0.0001,

l1_ratio: float = 0.15,

fit_intercept: bool = True,

epsilon: float = 0.1,

learning_rate: str = 'invscaling',

eta0: float = 0.01,

random_seed: int | None = None,

)

Construct stochastic gradient descent Regressor.

Parameters:

• schema – Describes the datastream’s structure.

• loss – The loss function to be used.

• penalty – The penalty (aka regularization term) to be used.

• alpha – Constant that multiplies the regularization term.

• l1_ratio – The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1. l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1. Only used if penalty is ‘elasticnet’. Values must be in the range [0.0, 1.0].

• fit_intercept – Whether the intercept (bias) should be estimated or not. If False, the data is assumed to be already centered.

• epsilon – Epsilon in the epsilon-insensitive loss functions; only if loss is ‘huber’, ‘epsilon_insensitive’, or ‘squared_epsilon_insensitive’. For ‘huber’, determines the threshold at which it becomes less important to get the prediction exactly right. For epsilon-insensitive, any differences between the current prediction and the correct label are ignored if they are less than this threshold.

• learning_rate – The size of the gradient step.

• eta0 – The initial learning rate for the ‘constant’, ‘invscaling’ or ‘adaptive’ schedules. The default value is 0.0 as eta0 is not used by the default schedule ‘optimal’.

• random_seed – Seed for reproducibility.

predict(instance: Instance) → float

train(instance: RegressionInstance)