binary_quantifier#

mlquantify.multiclass.binary_quantifier(_cls=None, *, strategy_attr='strategy')[source]#

Decorator to enable binary quantification extensions (One-vs-Rest or One-vs-One).

This decorator dynamically extends a quantifier class to handle multiclass quantification tasks by decomposing them into multiple binary subproblems, following either the One-vs-Rest (OvR) or One-vs-One (OvO) strategy.

It automatically replaces the class methods fit, predict, and aggregate with binary-aware versions from BinaryQuantifier, while preserving access to the original implementations via _original_fit, _original_predict, and _original_aggregate.

Parameters:

clsclass: A subclass of BaseQuantifier implementing standard binary quantification methods (fit, predict, and aggregate).
strategy_attrstr, default=”strategy”: Name of the attribute that stores the multiclass decomposition strategy.

Returns:

class: The same class with binary quantification capabilities added.

Examples

>>> from mlquantify.base import BaseQuantifier
>>> from mlquantify.multiclass import binary_quantifier

>>> @binary_quantifier(strategy_attr="strategy")
... class MyQuantifier(BaseQuantifier):
...     def fit(self, X, y):
...         # Custom binary training logic
...         self.classes_ = np.unique(y)
...         return self
...
...     def predict(self, X):
...         # Return dummy prevalences
...         return np.array([0.4, 0.6])
...
...     def aggregate(self, preds, y_train):
...         # Example aggregation method
...         return np.mean(preds, axis=0)

>>> qtf = MyQuantifier()
>>> qtf.strategy = 'ovr'  # or 'ovo'
>>> X = np.random.randn(10, 5)
>>> y = np.random.randint(0, 3, 10)
>>> qtf.fit(X, y)
MyQuantifier(...)
>>> qtf.predict(X)
array([...])