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)
-
order (list) – defaults to
NoneA list with the targets order in which to construct the chain. If
Nonethen 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.
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
Update the model with a set of features x and a label 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 0x7fda6d520150>
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 label of a set of features x.
Parameters
- x
Returns
The predicted label.
predict_proba_one
Predict the probability of each label for a dictionary of features x.
Parameters
- x
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