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