DySsyn#

class mlquantify.methods.aggregative.DySsyn(learner: BaseEstimator | None = None, measure: str = 'topsoe', merge_factor: ndarray | None = None, bins_size: ndarray | None = None, alpha_train: float = 0.5, n: int | None = None)[source]#

Synthetic Distribution y-Similarity (DySsyn).

This method works similarly to the DyS method, but instead of using the train scores, it generates them via MoSS (Model for Synthetic Scores). MoSS creates a spectrum of score distributions ranging from highly separated to fully mixed scores.

Parameters:

learnerBaseEstimator: A probabilistic classifier implementing the predict_proba method.
measurestr, optional: The metric used to compare distributions. Options are: - “hellinger” - “topsoe” - “probsymm” Default is “topsoe”.
merge_factornp.ndarray, optional: Array controlling the mixing level of synthetic distributions. Default is np.linspace(0.1, 0.4, 10).
bins_sizenp.ndarray, optional: Array of bin sizes for histogram computation. Default is np.append(np.linspace(2, 20, 10), 30).
alpha_trainfloat, optional: Initial estimate of the training prevalence. Default is 0.5.
nint, optional: Number of synthetic samples generated. Default is None.

Attributes:

bins_sizenp.ndarray: Bin sizes used for histogram calculations.
merge_factornp.ndarray: Mixing factors for generating synthetic score distributions.
alpha_trainfloat: True training prevalence.
nint: Number of samples generated during synthetic distribution creation.
measurestr: Selected distance metric.
mNone or float: Best mixing factor determined during computation.

References

MALETZKE, André et al. Accurately quantifying under score variability. In: 2021 IEEE International Conference on Data Mining (ICDM). IEEE, 2021. p. 1228-1233. Avaliable at https://ieeexplore.ieee.org/abstract/document/9679104

Examples

>>> from mlquantify.methods.mixture_models import DySsyn
>>> from mlquantify.utils.general import get_real_prev
>>> from sklearn.ensemble import RandomForestClassifier
>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.model_selection import train_test_split
>>>
>>> features, target = load_breast_cancer(return_X_y=True)
>>> X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)
>>>
>>> dyssyn = DySsyn(RandomForestClassifier())
>>> dyssyn.fit(X_train, y_train)
>>> prevalence = dyssyn.predict(X_test)
>>> prevalence
{0: 0.3606413872681201, 1: 0.6393586127318799}
>>> get_real_prev(y_test)
{0: 0.37719298245614036, 1: 0.6228070175438597}

GetMinDistancesDySsyn(test_scores: ndarray) → list[source]#

Calculates the minimum distances between test scores and synthetic distributions of MoSS across various bin sizes and merge factors.

Parameters:

test_scoresnp.ndarray: Array of predicted probabilities for the test data.

Returns:

valuesdict: Dictionary mapping each merge factor (m) to a tuple containing: - The minimum distance value. - The corresponding prevalence estimate.

best_distance(X_test)[source]#

Computes the minimum distance between test scores and synthetic distributions of MoSS.

Parameters:

X_testarray-like of shape (n_samples, n_features): Test data.

Returns:

distancefloat: Minimum distance value for the test data.

delayed_fit(class_, X, y)[source]#

Delayed fit method for one-vs-all strategy, with parallel execution.

Parameters:

class_Any: The class for which the model is being fitted.
Xarray-like: Training features.
yarray-like: Training labels.

Returns:

selfobject: Fitted binary quantifier for the given class.

delayed_predict(class_, X)[source]#

Delayed predict method for one-vs-all strategy, with parallel execution.

Parameters:

class_Any: The class for which the model is making predictions.
Xarray-like: Test features.

Returns:

float: Predicted prevalence for the given class.

fit(X, y, learner_fitted=False, cv_folds: int = 10, n_jobs: int = 1)[source]#

Fit the quantifier model.

Parameters:

Xarray-like: Training features.
yarray-like: Training labels.
learner_fittedbool, default=False: Whether the learner is already fitted.
cv_foldsint, default=10: Number of cross-validation folds.
n_jobsint, default=1: Number of parallel jobs to run.

Returns:

selfobject: The fitted quantifier instance.

Notes

The model dynamically determines whether to perform one-vs-all classification or to directly fit the data based on the type of the problem: - If the data is binary or inherently multiclass, the model fits directly using

_fit_method without creating binary quantifiers.

For other cases, the model creates one binary quantifier per class using the one-vs-all approach, allowing for dynamic prediction based on the provided dataset.

fit_learner(X, y)[source]#

Fit the learner to the training data.

Parameters:

Xarray-like: Training features.
yarray-like: Training labels.

get_distance(dist_train, dist_test, measure: str) → float[source]#

Computes the distance between training and test distributions using a specified metric.

Parameters:

dist_trainnp.ndarray: Distribution of scores for the training data.
dist_testnp.ndarray: Distribution of scores for the test data.
measurestr: The metric to use for distance calculation. Supported values are ‘topsoe’, ‘probsymm’, ‘hellinger’, and ‘euclidean’.

Returns:

float: The computed distance between the two distributions.

Raises:

ValueError: If the input distributions have mismatched sizes or are zero vectors.

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.

property is_multiclass: bool[source]#

Indicates whether the model supports multiclass classification.

Returns:

bool: Always returns False, as MixtureModel supports only binary classification.

property is_probabilistic: bool[source]#

Check if the learner is probabilistic or not.

Returns:

bool: True if the learner is probabilistic, False otherwise.

property learner[source]#: Returns the learner_ object. Returns ——- learner_ : object

The learner_ object stored in the class instance.

predict(X) → dict[source]#

Predict class prevalences for the given data.

Parameters:

Xarray-like: Test features.

Returns:

dict: A dictionary where keys are class labels and values are their predicted prevalences.

Notes

The prediction approach is dynamically chosen based on the data type: - For binary or inherently multiclass data, the model uses _predict_method to directly

estimate class prevalences.

For other cases, the model performs one-vs-all prediction, where each binary quantifier estimates the prevalence of its respective class. The results are then normalized to ensure they form valid proportions.

predict_learner(X)[source]#

Predict the class labels or probabilities for the given data.

Parameters:

Xarray-like: Test features.

Returns:

array-like: The predicted class labels or probabilities.

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see 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.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

cv_foldsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for cv_folds parameter in fit.
learner_fittedstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for learner_fitted parameter in fit.
n_jobsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for n_jobs parameter in fit.

Returns:

selfobject: The updated object.

set_params(**params)[source]#

Set the parameters of this estimator. The method allows setting parameters for both the model and the learner. Parameters that match the model’s attributes will be set directly on the model. Parameters prefixed with ‘learner__’ will be set on the learner if it exists. Parameters: ———– **params : dict

Dictionary of parameters to set. Keys can be model attribute names or ‘learner__’ prefixed names for learner parameters.

Returns:#

selfQuantifier: Returns the instance of the quantifier with updated parameters itself.

DySsyn#

Returns:#

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