OutputCodeClassifier¶
Outputcode multiclass strategy.
This also referred to as "errorcorrecting output codes".
This class allows to learn a multiclass classification problem with a binary classifier. Each class is converted to a code of 0s and 1s. The length of the code is called the code size. A copy of the classifier made for code. The codes associated with the classes are stored in a code book.
When a new sample arrives, the label's code is retrieved from the code book. Then, each classifier is trained on the relevant part of code, which is either a 0 or a 1.
For predicting, each classifier outputs a probability. These are then compared to each code in the code book, and the label which is the "closest" is chosen as the most likely class. Closeness is determined in terms of Manhattan distance.
One specificity of online learning is that we don't how many classes there are initially. Therefore, a random procedure generates random codes on the fly whenever a previously unseed label appears.
Parameters¶

classifier
Type → base.Classifier
A binary classifier, although a multiclass classifier will work too.

code_size
Type → int
The code size, which dictates how many copies of the provided classifiers to train. Must be strictly positive.

coding_method
Type → str
Default →
random
The method used to generate the codes. Can be either 'exact' or 'random'. The 'exact' method generates all possible codes of a given size in memory, and streams them in a random order. The 'random' method generates random codes of a given size on the fly. The 'exact' method necessarily generates different codes for each class, but requires more memory. The 'random' method can generate duplicate codes for different classes, but requires less memory.

seed
Type → int  None
Default →
None
A random seed number that can be set for reproducibility.
Examples¶
from river import datasets
from river import evaluate
from river import linear_model
from river import metrics
from river import multiclass
from river import preprocessing
dataset = datasets.ImageSegments()
scaler = preprocessing.StandardScaler()
ooc = multiclass.OutputCodeClassifier(
classifier=linear_model.LogisticRegression(),
code_size=10,
coding_method='random',
seed=1
)
model = scaler  ooc
metric = metrics.MacroF1()
evaluate.progressive_val_score(dataset, model, metric)
MacroF1: 79.58%
Methods¶
learn_one
Update the model with a set of features x
and a label y
.
Parameters
 x
 y
 kwargs
predict_one
Predict the label of a set of features x
.
Parameters
 x
 kwargs
Returns
The predicted label.
predict_proba_one
Predict the probability of each label for a dictionary of features x
.
Parameters
 x — 'dict'
Returns
dict[base.typing.ClfTarget, float]: A dictionary that associates a probability which each label.

Dietterich, T.G. and Bakiri, G., 1994. Solving multiclass learning problems via errorcorrecting output codes. Journal of artificial intelligence research, 2, pp.263286. ↩

James, G. and Hastie, T., 1998. The error coding method and PICTs. Journal of Computational and Graphical statistics, 7(3), pp.377387. ↩