AnDE#

class skbn.AnDE(n_dependence=1, n_bins=5, strategy='quantile', alpha=1.0, n_jobs=None, categorical_features=None, bernoulli_features=None, gaussian_features=None)#

Averaged n-Dependence Estimators (AnDE) [Generative].

This is the standard generative model described by Webb et al. [1]. It aggregates the predictions of sub-models (SPODEs) using an Arithmetic Mean.

\[P(y|x) \propto \sum_{i} P_i(y, x) \equiv \log \sum_{i} \exp(\text{JLL}_i)\]

This implementation extends the original AnDE by supporting mixed data types (Gaussian/Categorical) through the Super-Class strategy.

Parameters:

n_dependenceint, default=1: The order of dependence. - n=1: AODE (Averaged One-Dependence Estimators). - n=2: A2DE.
n_binsint, default=5: Bins for discretizing super-parents.
strategystr, default=’quantile’: Discretization strategy.
alphafloat, default=1.0: Smoothing parameter.

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 = '$UNCHANGED$') → AnDE#

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: