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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.

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 0x7f4d7f41a160>
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

Predict the output 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