SORD#

class mlquantify.matching.SORD(estimator=None, n_grid=101, strategy='ovr', cv=None, stratified=True, shuffle=False, random_state=None)[source]#

Sample Ordinal Distance (SORD) quantifier.

Targets prior probability shift. A parameter-free mixture model: instead of binning scores it compares the raw weighted score samples with an ordinal (earth-mover-like) distance, searching the mixture proportion that best blends the positive and negative training scores to match the test sample. Binary-only, with no bin count to tune; multiclass via one-vs-rest.

Parameters:

estimatorestimator, optional

A probabilistic classifier with fit and predict_proba methods.

n_gridint, default=101

Number of grid points for the prevalence (alpha) search.

strategy{‘ovr’, ‘ovo’}, default=’ovr’

Multiclass decomposition strategy.

'ovr' : one-vs-rest, one binary quantifier per class.
'ovo' : one-vs-one, one binary quantifier per class pair.

cvint or None, default=None

Cross-validation folds for computing training scores.

stratifiedbool, default=True

Whether to stratify CV splits.

shufflebool, default=False

Whether to shuffle data before splitting.

random_stateint or None, default=None

Random seed for reproducibility.

Attributes:

estimator_estimator: The fitted underlying classifier.
classes_ndarray of shape (n_classes,): Class labels seen during fit.

See also

DyS: Histogram mixture model with a selectable distance.
SMM: Mean-matching member of the same framework.

Notes

SORD is parameter-free (no bin count) and competitive with a tuned Topsoe-DyS. Its cost grows roughly as O(n log n) in the combined sample size, so training scores may be sub-sampled.

References

Examples

>>> from mlquantify.matching import SORD
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_samples=200, random_state=42)
>>> q = SORD(estimator=LogisticRegression()).fit(X, y)
>>> q.predict(X)
{0: ..., 1: ...}
>>> # call aggregate with pre-computed posterior scores
>>> scores = q.estimator_.predict_proba(X)
>>> q.aggregate(scores, scores, y)
{0: ..., 1: ...}

aggregate(predictions, y_train, classes=None)[source]#

Aggregate posteriors into prevalences using MoSS score simulation.

Searches over merging_factors to find the synthetic score distribution (generated by MoSS) whose histogram is closest to the test score distribution, then passes that synthetic set as the training reference to the base quantifier’s aggregate.

Parameters:

predictionsndarray of shape (n_samples, n_classes): Posterior probabilities of the test instances.
y_trainndarray of shape (n_train_samples,): Training class labels used to resolve class ordering.
classesarray-like of shape (n_classes,) or None, default=None: Class labels the output must report, in order. When None they are inferred from y_train.

Returns:

prevalencesdict or ndarray of shape (n_classes,): Estimated class prevalences.

Examples

>>> from mlquantify.meta import QuaDapt
>>> from mlquantify.matching import DyS
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_samples=200, random_state=42)
>>> q = QuaDapt(DyS(LogisticRegression())).fit(X, y)
>>> proba = LogisticRegression().fit(X, y).predict_proba(X)
>>> q.aggregate(proba, y)
{0: ..., 1: ...}

fit(X, y)[source]#

Fit the base classifier of the wrapped quantifier.

Only the underlying estimator is trained here; the full aggregation is deferred to aggregate so that the MoSS-based correction can be applied at prediction time.

Parameters:

Xarray-like of shape (n_samples, n_features): Training feature matrix.
yarray-like of shape (n_samples,): Training class labels.

Returns:

selfQuaDapt: The fitted quantifier.

Raises:

ValueError: If the wrapped quantifier does not use soft (probabilistic) predictions.

Examples

>>> from mlquantify.meta import QuaDapt
>>> from mlquantify.matching import DyS
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_samples=200, random_state=42)
>>> q = QuaDapt(DyS(LogisticRegression())).fit(X, y)

fit_predict(X, y, X_test)[source]#: Fit and predict class prevalences without storing models.

get_distance(dist_train, dist_test, distance='hellinger')[source]#: Compute a distance between two normalized representations.

get_metadata_routing()[source]#

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)[source]#

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)[source]#

Predict class prevalences using the MoSS adaptive correction.

Generates posterior probabilities for X with the fitted classifier and delegates to aggregate, which selects the best MoSS merging factor and calls the base quantifier’s aggregate.

Parameters:

Xarray-like of shape (n_samples, n_features): Test feature matrix.

Returns:

prevalencesdict or ndarray of shape (n_classes,): Estimated class prevalences.

Examples

>>> from mlquantify.meta import QuaDapt
>>> from mlquantify.matching import DyS
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_samples=200, random_state=42)
>>> q = QuaDapt(DyS(LogisticRegression())).fit(X, y)
>>> q.predict(X)
{0: ..., 1: ...}

save_quantifier(path: str | None = None) → None[source]#: Save the quantifier instance to a file.

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to fit.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

cv_predictionstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for cv_prediction parameter in fit.
estimator_fittedstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for estimator_fitted parameter in fit.
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in fit.

Returns:

selfobject: The updated object.

set_params(**params)[source]#

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.