SMM

class mlquantify.mixture.SMM(learner=None, strategy='ovr')

Sample Mean Matching (SMM) quantification method.

Estimates class prevalence by matching the mean score of the test samples to a convex combination of positive and negative training scores. The mixture weight \(\alpha\) is computed as:

\[\alpha = \frac{\bar{s}_{test} - \bar{s}_{neg}}{\bar{s}_{pos} - \bar{s}_{neg}}\]

where \(\bar{s}\) denotes the sample mean.

Parameters:

learnerestimator, optional

Base probabilistic classifier.

References

[2] Esuli et al. (2023). Learning to Quantify. Springer.

aggregate(*args)

Aggregate binary predictions to obtain multiclass prevalence estimates.

best_mixture(predictions, pos_scores, neg_scores)

Determine the best mixture parameters for the given data.

fit(X, y)

Fit the quantifier under a binary decomposition strategy.

get_best_distance(*args, **kwargs)

Get the best distance value from the mixture fitting process.

Notes

If the quantifier has not been fitted yet, it will fit the model for getting the best distance.

classmethod get_distance(dist_train, dist_test, measure='hellinger')

Compute distance between two distributions.

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.

predict(X)

Predict class prevalences using the trained binary quantifiers.

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.