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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

    Typebase.Classifier

    A classifier model used for each label.

  • order

    Typelist | None

    DefaultNone

    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, parser='auto', 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): 41.81%

Methods

learn_one

Update the model with a set of features x and the labels y.

Parameters

  • x
  • y
  • kwargs

Returns

self

predict_one

Predict the labels of a set of features x.

Parameters

  • x'dict'
  • kwargs

Returns

dict[FeatureName, 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.