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RegressorChain

A multi-output model that arranges regressor into a chain.

This will create one model per output. The prediction of the first output will be used as a feature in the second output. The prediction for the second output will be used as a feature for the third, etc. This "chain model" is therefore capable of capturing dependencies between outputs.

Parameters

  • model (base.Regressor)

  • order (list) – defaults to None

    A list with the targets order in which to construct the chain. If None then the order will be inferred from the order of the keys in the target.

Examples

>>> from river import evaluate
>>> from river import linear_model
>>> from river import metrics
>>> from river import multioutput
>>> from river import preprocessing
>>> from river import stream
>>> from sklearn import datasets

>>> dataset = stream.iter_sklearn_dataset(
...     dataset=datasets.load_linnerud(),
...     shuffle=True,
...     seed=42
... )

>>> model = multioutput.RegressorChain(
...     model=(
...         preprocessing.StandardScaler() |
...         linear_model.LinearRegression(intercept_lr=0.3)
...     ),
...     order=[0, 1, 2]
... )

>>> metric = metrics.multioutput.MicroAverage(metrics.MAE())

>>> evaluate.progressive_val_score(dataset, model, metric)
MicroAverage(MAE): 12.649592

Methods

clear

D.clear() -> None. Remove all items from D.

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
fromkeys
get

D.get(k[,d]) -> D[k] if k in D, else d. d defaults to None.

Parameters

  • key
  • default – defaults to None
items

D.items() -> a set-like object providing a view on D's items

keys

D.keys() -> a set-like object providing a view on D's keys

learn_one

Fits to a set of features x and a real-valued target y.

Parameters

  • x
  • y

Returns

self

pop

D.pop(k[,d]) -> v, remove specified key and return the corresponding value. If key is not found, d is returned if given, otherwise KeyError is raised.

Parameters

  • key
  • default – defaults to <object object at 0x7f7c2531b150>
popitem

D.popitem() -> (k, v), remove and return some (key, value) pair as a 2-tuple; but raise KeyError if D is empty.

predict_one

Predicts the target value of a set of features x.

Parameters

  • x

Returns

The prediction.

setdefault

D.setdefault(k[,d]) -> D.get(k,d), also set D[k]=d if k not in D

Parameters

  • key
  • default – defaults to None
update

D.update([E, ]**F) -> None. Update D from mapping/iterable E and F. If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v

Parameters

  • other – defaults to ()
  • kwds
values

D.values() -> an object providing a view on D's values