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

---

1. Online Learning from Experts: Weighed Majority and Hedge ↩

2. Wikipedia page on the multiplicative weight update method ↩

3. Kivinen, J. and Warmuth, M.K., 1997. Exponentiated gradient versus gradient descent for linear predictors. information and computation, 132(1), pp.1-63. ↩