WeightedAnDE

class skbn.WeightedAnDE(n_dependence=1, n_bins=5, strategy='quantile', alpha=1.0, l2_reg=0.0001, max_iter=100, weight_level=1, n_jobs=None, categorical_features=None, bernoulli_features=None, gaussian_features=None, modular=False)

Weighted Averaged n-Dependence Estimators (WeightedAnDE) [Hybrid].

A discriminative weighting of the standard AnDE (Arithmetic Mean) ensemble. Unlike ALR which operates on geometric means in log-space, WeightedAnDE optimizes weights in probability space (arithmetic mean), making the joint optimization non-convex.

Supports 4 Levels of Weight Granularity (from coarsest to finest): 1. Per Model (Default) - One weight per ensemble member. 2. Per Parent Value - One weight per parent feature value. 3. Per Class - One weight per target class per model. 4. Per Parent Value & Class - One weight per parent value per class.

Parameters:

n_dependenceint, default=1

The number of parent features conditioned upon (n-dependence). 0 corresponds to Naive Bayes topology.

alphafloat, default=1.0

Additive (Laplace/Lidstone) smoothing parameter for probabilities.

categorical_featuresarray-like of int or str, default=”auto”

Indices or mask of categorical features. “auto” detects types.

discretization_strategy{“tree”, “quantile”, “uniform”}, default=”tree”

Strategy to discretize continuous features.

n_binsint, default=5

Maximum number of bins for discretization.

weight_levelint, default=1

Granularity of weights (1-4).

l2_regfloat, default=1e-4

L2 regularization applied during weight optimization.

random_stateint, RandomState instance or None, default=None

Controls random seed for random operations like discretization.

n_jobsint, default=None

Number of parallel jobs to run during inference and optimization.

Methods

fit(X, y)	Generative fitting.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
predict(X)
predict_log_proba(X)
predict_proba(X)
score(X, y[, sample_weight])	Return accuracy on provided data and labels.
set_params(**params)	Set the parameters of this estimator.
set_score_request(*[, sample_weight])	Configure whether metadata should be requested to be passed to the score method.

fit(X, y)

Generative fitting. Learns the joint probability P(y, x) for each subspace (SPODE) by counting frequencies.

Parameters:

Xarray-like of shape (n_samples, n_features)

Training vectors.

yarray-like of shape (n_samples,)

Target values.

Returns:

selfobject

Returns the instance itself.

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.

score(X, y, sample_weight=None)

Return accuracy on provided data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters:

Xarray-like of shape (n_samples, n_features)

Test samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True labels for X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns:

scorefloat

Mean accuracy of self.predict(X) w.r.t. y.

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.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → WeightedAnDE

Configure whether metadata should be requested to be passed to the score 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 score 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 score.

• 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:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in score.

Returns:

selfobject

The updated object.