PassiveAggressiveRegressor

class capymoa.regressor.PassiveAggressiveRegressor

Bases: SKRegressor

Streaming Passive Aggressive regressor

This wraps sklearn.linear_model.PassiveAggressiveRegressor for ease of use in the streaming context. Some options are missing because they are not relevant in the streaming context.

Reference:

Online Passive-Aggressive Algorithms K. Crammer, O. Dekel, J. Keshat, S. Shalev-Shwartz, Y. Singer - JMLR (2006)

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 = PassiveAggressiveRegressor(schema)
>>> results = prequential_evaluation(stream, learner, max_instances=1000)
>>> results["cumulative"].rmse()
3.7004531627005455

sklearner: PassiveAggressiveRegressor

The underlying scikit-learn object. See: sklearn.linear_model.PassiveAggressiveRegressor

__init__(

schema: Schema,

max_step_size: float = 1.0,

fit_intercept: bool = True,

loss: str = 'epsilon_insensitive',

average: bool = False,

random_seed=1,

)

Construct a passive aggressive regressor.

Parameters:

• schema – Stream schema

• max_step_size – Maximum step size (regularization).

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

• loss –

  The loss function to be used:

  • "epsilon_insensitive": equivalent to PA-I in the reference paper.

  • "squared_epsilon_insensitive": equivalent to PA-II in the reference paper.

• average – When set to True, computes the averaged SGD weights and stores the result in the sklearner.coef_ attribute. If set to an int greater than 1, averaging will begin once the total number of samples seen reaches average. So average=10 will begin averaging after seeing 10 samples.

• random_seed – Seed for the random number generator.

predict(instance: Instance) → float

train(instance: RegressionInstance)