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RegressorChain

A multi-output model that arranges regressors 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

    Type → base.Regressor

    The regression model used to make predictions for each target.

  • order

    Type → list | None

    Default → 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
  • kwargs

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 0x7fb73fcc8180>

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 outputs of features x.

Parameters

  • x
  • kwargs

Returns

The predictions.

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