MatchingKernelQuantifier

class mlquantify.matching.MatchingKernelQuantifier(kernel='rbf', gamma=None, degree=3, coef0=0.0, solver='slsqp')

Abstract base class for kernel mean matching quantifiers.

Estimates class prevalences by minimising the distance between the kernel mean embedding of the test data and the mixture of class-conditional kernel mean embeddings computed on training data.

Parameters:

kernelstr, default=’rbf’

Kernel function to use. One of 'rbf', 'linear', 'poly', 'sigmoid', 'cosine'.

gammafloat or None, default=None

Kernel coefficient for 'rbf', 'poly', and 'sigmoid'. If None, uses 1 / n_features.

degreeint, default=3

Polynomial degree for the 'poly' kernel.

coef0float, default=0.0

Independent term for 'poly' and 'sigmoid' kernels.

solverstr, default=’slsqp’

Optimization solver.

Attributes:

classes_ndarray of shape (n_classes,)

Class labels seen during fit.

Examples

>>> from mlquantify.matching._kernel import MatchingKernelQuantifier
>>> from sklearn.datasets import make_classification
>>> import numpy as np
>>> class MyKernelQ(MatchingKernelQuantifier):
...     def fit(self, X, y):
...         self.classes_ = np.unique(y)
...         return self._fit(X, y)
...     def predict(self, X):
...         return self._predict(X)
>>> X, y = make_classification(n_samples=200, random_state=42)
>>> MyKernelQ().fit(X, y).predict(X)
{0: 0.49, 1: 0.51}

get_distance(dist_train, dist_test, distance='hellinger')

Compute a distance between two normalized representations.

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict

Parameter names mapped to their values.

save_quantifier(path: str | None = None) → None

Save the quantifier instance to a file.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.