EWARegressor¶

Exponentially Weighted Average regressor.

Parameters¶

regressors (List[base.Regressor])

The regressors to hedge.
loss (optim.losses.RegressionLoss) – defaults to None

The loss function that has to be minimized. Defaults to optim.losses.Squared.
learning_rate – defaults to 0.5

The learning rate by which the model weights are multiplied at each iteration.

Attributes¶

regressors

Examples¶

>>> from river import datasets
>>> from river import evaluate
>>> from river import expert
>>> from river import linear_model
>>> from river import metrics
>>> from river import optim
>>> from river import preprocessing
>>> from river import stream

>>> optimizers = [
...     optim.SGD(0.01),
...     optim.RMSProp(),
...     optim.AdaGrad()
... ]

>>> for optimizer in optimizers:
...
...     dataset = datasets.TrumpApproval()
...     metric = metrics.MAE()
...     model = (
...         preprocessing.StandardScaler() |
...         linear_model.LinearRegression(
...             optimizer=optimizer,
...             intercept_lr=.1
...         )
...     )
...
...     print(optimizer, evaluate.progressive_val_score(dataset, model, metric))
SGD MAE: 0.555971
RMSProp MAE: 0.528284
AdaGrad MAE: 0.481461

>>> dataset = datasets.TrumpApproval()
>>> metric = metrics.MAE()
>>> hedge = (
...     preprocessing.StandardScaler() |
...     expert.EWARegressor(
...         regressors=[
...             linear_model.LinearRegression(optimizer=o, intercept_lr=.1)
...             for o in optimizers
...         ],
...         learning_rate=0.005
...     )
... )

>>> evaluate.progressive_val_score(dataset, hedge, metric)
MAE: 0.494832

Methods¶

clone

Return a fresh estimator with the same parameters.

The clone has the same parameters but has not been updated with any data. This works by looking at the parameters from the class signature. Each parameter is either - recursively cloned if it's a River classes. - deep-copied via copy.deepcopy if not. If the calling object is stochastic (i.e. it accepts a seed parameter) and has not been seeded, then the clone will not be idempotent. Indeed, this method's purpose if simply to return a new instance with the same input parameters.

learn_one

Fits to a set of features x and a real-valued target y.

Parameters

x
y

Returns

self

learn_predict_one

predict_one

Predicts the target value of a set of features x.