UCBRegressor

Model selection based on the UCB bandit strategy.

Due to the nature of this algorithm, it's recommended to scale the target so that it exhibits sub-gaussian properties. This can be done by using a preprocessing.TargetStandardScaler.

Parameters

• models

  The models to choose from.

• metric – defaults to None

  The metric that is used to compare models with each other. Defaults to metrics.MAE.

• delta – defaults to 1

  Exploration parameter.

• burn_in – defaults to 100

  The number of initial steps during which each model is updated.

• seed (int) – defaults to None

  Random number generator seed for reproducibility.

Attributes

• best_model

  The current best model.

• burn_in

• delta

• models

• seed

Examples

>>> from river import datasets
>>> from river import evaluate
>>> from river import linear_model
>>> from river import metrics
>>> from river import model_selection
>>> from river import optim
>>> from river import preprocessing

>>> models = [
...     linear_model.LinearRegression(optimizer=optim.SGD(lr=lr))
...     for lr in [0.0001, 0.001, 1e-05, 0.01]
... ]

>>> dataset = datasets.TrumpApproval()
>>> model = (
...     preprocessing.StandardScaler() |
...     preprocessing.TargetStandardScaler(
...         model_selection.UCBRegressor(
...             models,
...             delta=1,
...             burn_in=0,
...             seed=42
...         )
...     )
... )
>>> metric = metrics.MAE()

>>> evaluate.progressive_val_score(dataset, model, metric)
MAE: 0.410815

>>> model['TargetStandardScaler'].regressor.bandit
Ranking   MAE        Pulls   Share
     #3   1.441458       8    0.80%
     #1   0.291200     242   24.18%
     #2   0.808878      19    1.90%
     #0   0.204892     732   73.13%

>>> model['TargetStandardScaler'].regressor.best_model
LinearRegression (
  optimizer=SGD (
    lr=Constant (
      learning_rate=0.01
    )
  )
  loss=Squared ()
  l2=0.
  intercept_init=0.
  intercept_lr=Constant (
    learning_rate=0.01
  )
  clip_gradient=1e+12
  initializer=Zeros ()
)

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 (dict)
• y (numbers.Number)

Returns

Regressor: self

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. Lai, T. L., & Robbins, H. (1985). Asymptotically efficient adaptive allocation rules. Advances in applied mathematics, 6(1), 4-22. ↩

2. Upper Confidence Bounds - The Multi-Armed Bandit Problem and Its Solutions - Lilian Weng ↩

3. The Upper Confidence Bound Algorithm - Bandit Algorithms ↩