sklvq.models.LVQBaseClass

class sklvq.models.LVQBaseClass(distance_type: str | type = 'squared-euclidean', distance_params: dict | None = None, valid_distances: list[str] | None = None, solver_type: str | type = 'steepest-gradient-descent', solver_params: dict | None = None, valid_solvers: list[str] | None = None, prototype_init: str = 'class-conditional-mean', prototype_n_per_class: int = 1, random_state: int | RandomState = None, force_all_finite: str | bool = True)

Learning Vector Quantization base class

Abstract class for implementing LVQ models. It provides abstract methods with expected call signatures.

Provides a common interface to the solver and other function that require access to the models. Additionally, it implements a number of functions shared by the currently implemented LVQ variations.

See also

GLVQ, GMLVQ, LGMLVQ

__init__(distance_type: str | type = 'squared-euclidean', distance_params: dict | None = None, valid_distances: list[str] | None = None, solver_type: str | type = 'steepest-gradient-descent', solver_params: dict | None = None, valid_solvers: list[str] | None = None, prototype_init: str = 'class-conditional-mean', prototype_n_per_class: int = 1, random_state: int | RandomState = None, force_all_finite: str | bool = True)

abstract add_partial_gradient(gradient, partial_gradient, i_prototype) → None

To increase performance, the distance gradient methods (should) return only the relevant values. I.e., the gradient of the prototype i_prototype and potentially other parameters linked to this prototype. This partial gradient needs to added (overwrite) to the correct parts of the actual gradient and this is what this function should do.

Parameters:

gradientndarray

Same shape as the get_variables() would return.

partial_gradientndarray

1d array containing the partial gradient.

i_prototypeint

The index of the prototype to which the partial gradient was computed.

decision_function(X: ndarray)

Evaluates the decision function for the samples in X. Shape for binary class is (n_observations,) with the decision values for the “greater” class. In the multiclass case it returns decision values for each class and therefore has the shape (n_observations, n_classes).

Parameters:

Xndarray

The data.

Returns:

decision_valuesndarray

Binary case shape is (n_observations,) and the multiclass case (n_observations, n_classes)

fit(X: ndarray, y: ndarray)

Fit function that provides the general implementation of the LVQ algorithms. It checks the data, calls

before_fit method, calls the solve method of the solver, and the after_fit method.

Parameters:

Xndarray of shape (number of observations, number of dimensions)

yndarray of size (number of observations)

Returns:

self

The trained model

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.

abstract get_model_params() → tuple | ndarray

Should return a view or tuple of views (in correct shape) of the model’s parameters. Implementation depends on specific model as model parameters may differ per model.

Returns:

ndarray or tuple

View or tuple of views of the model’s parameters.

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.

get_prototypes() → ndarray

Return a view into self._variables of the the shape of the prototypes (n_prototypes, n_features). At the moment only consistency function, does not actually create the shape and only works after self.prototypes_ has been set.

Returns:

prototypesndarray of shape (n_prototypes, n_features)

View into self._variables with shape specified above.

get_variables() → ndarray

Returns the self._variables array that owns the memory allocated for the model parameters.

Returns:

_variablesndarray

returns the model’s _variables array.

abstract mul_step_size(step_size: float | ndarray, gradient: ndarray) → None

Should multiply the provided gradient with the provided step size and overwrite the values in gradient. Depending on the step_size being a float or array different step sizes are used for different model parameters (which also depends on the model if there are more then only prototypes)

Parameters:

step_sizefloat or ndarray

The scalar or list of values containing the step sizes.

gradientndarray

Same shape as the get_variables() would return.

abstract normalize_variables(var_buffer: ndarray) → None

Should modify the var_buffer as if it was the variables array provided by get_variables().

Parameters:

var_bufferndarray

Array with the same size as the model’s variables array as returned by get_variables().

Returns:

ndarray or tuple

Same shape and size as input, but normalized. How to normalize depends on model implementation.

predict(X: ndarray, threshold: float = 0.5)

Predict function

The decision is made for the label of the prototype with the minimum decision value, as provided by the decision_function(). To choose a different cutoff point (e.g., based upon ROC analysis for 2-class data), threshold can be set, which acts on the values from predict_proba(). The threshold value won’t be used when the model has been fitted to data with more than 2 classes.

Parameters:

Xndarray

The data.

thresholdfloat

threshold for predict_proba() values (default 0.5), should be in the interval [0,1]

Returns:

ndarray of shape (n_observations)

Returns the predicted labels.

predict_proba(X: ndarray)

Parameters:

Xndarray

The data.

Returns:

confidence_scoresndarray of shape (n_observations, n_classes)

score(X, y, sample_weight=None)

Return the mean accuracy on the given test 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.

abstract set_model_params(new_model_params: tuple | ndarray)

Should modify the self._variables array. Accepts the new_model_params in the shape of the model’s parameters, e.g., prototypes or (prototypes, relevance_matrix).

Always needs to be all the model parameters, can not be used for partial updates.

Parameters:

new_model_paramsndarray or tuple

Array or tuple of arrays of the new model’s parameters.

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_predict_request(*, threshold: bool | None | str = '$UNCHANGED$') → LVQBaseClass

Request metadata passed to the predict 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 predict 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 predict.

• 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:

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

Metadata routing for threshold parameter in predict.

Returns:

selfobject

The updated object.

set_prototypes(new_prototypes: ndarray) → None

Accepts a new_prototypes array with the same shape as self.prototypes_ and overwrites the self._variables array by copying the values of the new_prototypes.

Parameters:

new_prototypesndarray of shape (n_prototypes, n_features)

The new prototypes the model should store.

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

Request metadata passed to the score 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 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.

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:

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.

set_variables(new_variables: ndarray) → None

Modifies the self._variables by copying the values of new_variables into the memory of self._variables.

Parameters:

new_variablesndarray

1d numpy array that contains all the model parameters in continuous memory

abstract to_model_params_view(var_buffer: ndarray) → tuple | ndarray

Should return a single view into the var_buffer or a tuple of views. This depends on the model and its parameters.

Parameters:

var_bufferndarray

Array with the same size as the model’s variables array as returned by get_variables().

Returns:

ndarray or tuple

Should return a view or tuple of views of the model parameters in appropriate shapes.

abstract to_prototypes_view(var_buffer: ndarray) → ndarray

Should return the prototypes from the provided var_buffer. I.e., it selects/views the appropriate part of memory and reshapes it.

Parameters:

var_bufferndarray

Array with the same size as the model’s variables array as returned by get_variables().

Returns:

ndarray of shape (n_prototypes, n_features)

View into the var_buffer.

Examples using sklvq.models.LVQBaseClass

Generalized LVQ (GLVQ)

Generalized LVQ (GLVQ)

Generalized Matrix LVQ (GMLVQ)

Generalized Matrix LVQ (GMLVQ)

Local Generalized Matrix LVQ (LGMLVQ)

Local Generalized Matrix LVQ (LGMLVQ)

Not a Number LVQ (NaNLVQ)

Not a Number LVQ (NaNLVQ)

Pipelines

Pipelines

Cross validation

Cross validation

Grid Search

Grid Search

Learning Behaviour

Learning Behaviour

Receiver Operating Characteristic Curve

Receiver Operating Characteristic Curve

Activation Functions

Activation Functions

Distance Functions

Distance Functions

Discriminant Functions

Discriminant Functions

Solvers

Solvers