EWARegressor¶

Exponentially Weighted Average regressor.

Parameters¶

models

Type → list[base.Regressor]

The regressors to hedge.
loss

Type → optim.losses.RegressionLoss | None

Default → None

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

Default → 0.5

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

Attributes¶

models

Examples¶

from river import datasets
from river import ensemble
from river import evaluate
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() |
    ensemble.EWARegressor(
        [
            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¶

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

Predict the output of features x.

Parameters

x

Returns

The prediction.