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ProbabilisticClassifierChain

Probabilistic Classifier Chains.

The Probabilistic Classifier Chains (PCC) 1 is a Bayes-optimal method based on the Classifier Chains (CC).

Consider the concept of chaining classifiers as searching a path in a binary tree whose leaf nodes are associated with a label \(y \in Y\). While CC searches only a single path in the aforementioned binary tree, PCC looks at each of the \(2^l\) paths, where \(l\) is the number of labels. This limits the applicability of the method to data sets with a small to moderate number of labels. The authors recommend no more than about 15 labels for real-world applications.

Parameters

Examples

from river import linear_model
from river import metrics
from river import multioutput
from river.datasets import synth

dataset = synth.Logical(seed=42, n_tiles=100)

model = multioutput.ProbabilisticClassifierChain(
    model=linear_model.LogisticRegression()
)

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

for x, y in dataset:
   y_pred = model.predict_one(x)
   y_pred = {k: y_pred.get(k, 0) for k in y}
   metric = metric.update(y, y_pred)
   model = model.learn_one(x, y)

metric

MicroAverage(Jaccard): 51.84%

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 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 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.

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

  1. Cheng, W., Hüllermeier, E., & Dembczynski, K. J. (2010). Bayes optimal multilabel classification via probabilistic classifier chains. In Proceedings of the 27th international conference on machine learning (ICML-10) (pp. 279-286). ↩