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EWARegressor

Exponentially Weighted Average regressor.

Parameters

  • models (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

  • 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

append

S.append(value) -- append value to the end of the sequence

Parameters

  • item
clear

S.clear() -> None -- remove all items from S

copy
count

S.count(value) -> integer -- return number of occurrences of value

Parameters

  • item
extend

S.extend(iterable) -- extend sequence by appending elements from the iterable

Parameters

  • other
index

S.index(value, [start, [stop]]) -> integer -- return first index of value. Raises ValueError if the value is not present.

Supporting start and stop arguments is optional, but recommended.

Parameters

  • item
  • args
insert

S.insert(index, value) -- insert value before index

Parameters

  • i
  • item
learn_one

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

Parameters

  • x
  • y

Returns

self

learn_predict_one
pop

S.pop([index]) -> item -- remove and return item at index (default last). Raise IndexError if list is empty or index is out of range.

Parameters

  • i – defaults to -1
predict_one

Predict the output of features x.

Parameters

  • x

Returns

The prediction.

remove

S.remove(value) -- remove first occurrence of value. Raise ValueError if the value is not present.

Parameters

  • item
reverse

S.reverse() -- reverse IN PLACE

sort

References