3.3. Building a Non-Aggregative Quantifier

A non-aggregative quantifier does use a mid task, but tries to estimate the class distribution of the test set directly from the training set. For implementing new non-aggregative quantifiers you must subclass from mlquantify.base.NonAggregativeQuantifier class, and implement only the _fit_method and _predict_method methods.

The _fit_method method is used to train the quantifier on a training set, while the _predict_method method is used to make predictions on a test set. The _fit_method method should return self to allow for method chaining. And the predictions should be a disctionary containing the predicted proportions for each class. The keys of the dictionary should be the class labels, sorted in ascending order. The values should be the predicted proportions for each class, which should sum to 1.0.

Note

Recommended Usage

When using quantifiers that subclass from mlquantify.base.NonAggregativeQuantifier to fit and predict data, you must use the fit and predict, where these methods will call the _fit_method and _predict_method methods respectively.

New non-aggregative quantifiers can be implemented the following way:

from mlquantify.base import NonAggregativeQuantifier

class MyNonAggregativeQuantifier(NonAggregativeQuantifier):

    def __init__(self, param1, param2):
        self.param1 = param1
        self.param2 = param2

    def _fit_method(self, X, y):
        # Fit the quantifier to the training data
        return self

    def _predict_method(self, X):
        # Make predictions on the test data
        return {0: 0.5, 1: 0.5}  # Example prediction