BanditRegressor¶
Bandit-based model selection.
Each model is associated with an arm. At each learn_one call, the policy decides which arm/model to pull. The reward is the performance of the model on the provided sample. The predict_one method uses the current best model.
Parameters¶
-
models
The models to select from.
-
metric (river.metrics.base.RegressionMetric)
The metric that is used to measure the performance of each model.
-
policy (river.bandit.base.Policy)
The bandit policy to use.
Attributes¶
-
best_model
The current best model.
-
models
Examples¶
>>> from river import bandit
>>> 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() |
... model_selection.BanditRegressor(
... models,
... metric=metrics.MAE(),
... policy=bandit.EpsilonGreedy(
... epsilon=0.1,
... decay=0.001,
... burn_in=100,
... seed=42
... )
... )
... )
>>> metric = metrics.MAE()
>>> evaluate.progressive_val_score(dataset, model, metric)
MAE: 2.544434
Here's another example using the UCB policy. The latter is more sensitive to the target scale, and usually works better when the target is rescaled.
>>> models = [
... linear_model.LinearRegression(optimizer=optim.SGD(lr=lr))
... for lr in [0.0001, 0.001, 1e-05, 0.01]
... ]
>>> model = (
... preprocessing.StandardScaler() |
... preprocessing.TargetStandardScaler(
... model_selection.BanditRegressor(
... models,
... metric=metrics.MAE(),
... policy=bandit.UCB(
... delta=1,
... burn_in=100
... )
... )
... )
... )
>>> metric = metrics.MAE()
>>> evaluate.progressive_val_score(dataset, model, metric)
MAE: 0.857745
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 (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
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