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ClassifierChain

A multi-output model that arranges classifiers 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 model. The prediction for the second output will be used as a feature for the third model, etc. This "chain model" is therefore capable of capturing dependencies between outputs.

Parameters

  • model (base.Classifier)

    A classifier model used for each label.

  • 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 feature_selection
>>> 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.fetch_openml('yeast', version=4, as_frame=False),
...     shuffle=True,
...     seed=42
... )

>>> model = feature_selection.VarianceThreshold(threshold=0.01)
>>> model |= preprocessing.StandardScaler()
>>> model |= multioutput.ClassifierChain(
...     model=linear_model.LogisticRegression(),
...     order=list(range(14))
... )

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

>>> for x, y in dataset:
...     # Convert y values to booleans
...     y = {i: yi == 'TRUE' for i, yi in y.items()}
...     y_pred = model.predict_one(x)
...     metric = metric.update(y, y_pred)
...     model = model.learn_one(x, y)

>>> metric
MicroAverage(Jaccard): 41.95%

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

Update the model with a set of features x and the labels 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 0x7fb8afa29160>
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 labels of a set of features x.

Parameters

  • x (dict)
  • kwargs

Returns

typing.Dict[typing.Hashable, bool]: The predicted labels.

predict_proba_one

Predict the probability of each label appearing given dictionary of features x.

Parameters

  • x
  • kwargs

Returns

A dictionary that associates a probability which each label.

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

References