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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

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.

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

Predicts the target value of a set 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