3.2. Building an Aggregative Quantifier#

If you are building a new quantifier that aggregates the results of a mid task, such as classification, you can subclass the AggregativeQuantifier class. This class provides two methods that must be implemented by subclasses, including the _fit_method and _predict_method methods. where in the _fit_method you can implement the fitting of the quantifier to the training data the way you want, and in the _predict_method you can implement the prediction of the quantifier to the test data, returning always a dictionary containing the predicted proportions for each class.

Note

Dynamic Label Scheme

In case your quantifier is a binary quantifier, you must include the is_multiclass property returning False, and in other case, you don’t need to include it.

Note

Method functionality

If your quantifier fits a learner (e.g., a classifier) in the _fit_method, you can include the is_probabilistic property returning False if the learner is not probabilistic, and nothing if it is probabilistic. This will allow you to use the fit_learner and predict_learner methods to fit and predict the learner for dinamically handling of multiple scenarios, including the Non parameters Scenarios, see Non Parameters Scenarios for more information.

An aggregative quantifier can be implemented the following way:

from mlquantify.base import AggregativeQuantifier
from sklearn.ensemble import RandomForestClassifier

class MyAggregativeQuantifier(AggregativeQuantifier):
    def is_multiclass(self):
        return False  # Doesn't need to be included if True

    def is_probabilistic(self):
        return False  # Doesn't need to be included if True

    def __init__(self, learner=RandomForestClassifier(), param1=None, param2=None):
        self.learner = learner
        self.param1 = param1
        self.param2 = param2

    def _fit_method(self, X, y):

        self.fit_learner(X, y)

        return self

    def _predict_method(self, X):
        # Make predictions on the test data
        y_pred = self.predict_learner(X)  # Example usage of learner prediction

        return {0: 0.5, 1: 0.5}  # Example prediction

Warning

Recommended usage

When implementing a new aggregative quantifier, it is recommended to use learner as one of the parameters, and it must have fit, predict and predict_proba methods. When using quantifiers that subclass from mlquantify.base.AggregativeQuantifier 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.

3.2. Building an Aggregative Quantifier#

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