`sklearn_rvm.em_rvm`.EMRVC¶

class sklearn_rvm.em_rvm.EMRVC(n_iter_posterior=50, kernel='rbf', degree=3, gamma='auto_deprecated', coef0=0.0, tol=0.001, threshold_alpha=1000000000.0, beta_fixed='not_fixed', alpha_max=10000000000.0, init_alpha=None, bias_used=True, max_iter=5000, compute_score=False, epsilon=1e-08, verbose=False)[source]¶

Relevance Vector Classifier.

Implementation of Mike Tipping”s Relevance Vector Machine for classification using the scikit-learn API.

The multiclass support is handled according to a one-vs-rest scheme.

For details on the precise mathematical formulation of the provided kernel functions and how gamma, coef0 and degree affect each other, see the corresponding section in the narrative documentation: Kernel functions.

Parameters

n_iter_posteriorint, optional (default=50)

Number of iterations to calculate posterior.

kernelstring, optional (default=”rbf”)

Specifies the kernel type to be used in the algorithm. It must be one of “linear”, “poly”, “rbf”, “sigmoid” or ‘precomputed’. If none is given, “rbf” will be used.

degreeint, optional (default=3)

Degree of the polynomial kernel function (“poly”). Ignored by all other kernels.

gammafloat, optional (default=”auto”)

Kernel coefficient for “rbf”, “poly” and “sigmoid”.

Current default is “auto” which uses 1 / n_features, if gamma="scale" is passed then it uses 1 / (n_features * X.var()) as value of gamma. The current default of gamma, “auto”, will change to “scale” in version 0.22. “auto_deprecated”, a deprecated version of “auto” is used as a default indicating that no explicit value of gamma was passed.

coef0float, optional (default=0.0)

Independent term in kernel function. It is only significant in “poly” and “sigmoid”.

tolfloat, optional (default=1e-6)

Tolerance for stopping criterion.

threshold_alphafloat, optional (default=1e5)

Threshold for alpha selection criterion.

beta_fixed{“not_fixed”} or float, optional (default=”not_fixed”)

Fixed value for beta. If “not_fixed” selected, the beta is updated at each iteration.

alpha_maxint, optional (default=1e9)

Basis functions associated with alpha value beyond this limit will be purged. Must be a positive and big number.

init_alphaarray-like of shape (n_sample) or None, optional (default=None)

Initial value for alpha. If None is selected, the initial value of alpha is defined by init_alpha = 1 / M ** 2.

bias_usedboolean, optional (default=False)

Specifies if a constant (a.k.a. bias) should be added to the decision function.

max_iterint, optional (default=5000)

Hard limit on iterations within solver.

compute_scoreboolean, optional (default=False)

Specifies if the objective function is computed at each step of the model.

verboseboolean, optional (default=False)

Enable verbose output.

See also

EMRVR: Relevant Vector Machine for Regression.

Notes

References: The relevance vector machine.

Attributes

relevance_array-like, shape (n_relevance): Indices of relevance vectors.
relevance_vectors_array-like, shape (n_relevance, n_features): Relevance vectors (equivalent to X[relevance_]).
alpha_array-like, shape (n_samples): Estimated alpha values.
gamma_array-like, shape (n_samples): Estimated gamma values.
Phi_array-like, shape (n_samples, n_features): Estimated phi values.
Sigma_array-like, shape (n_samples, n_features): Estimated covariance matrix of the weights.
mu_array-like, shape (n_relevance, n_features): Coefficients of the classifier (mean of posterior distribution)
coef_array, shape (n_class * (n_class-1) / 2, n_features): Coefficients of the classfier (mean of posterior distribution). Weights assigned to the features. This is only available in the case of a linear kernel. coef_ is a readonly property derived from mu and relevance_vectors_.

Methods

`fit`(self, X, y)	Fit the RVC model according to the given training data.
`get_params`(self[, deep])	Get parameters for this estimator.
`predict`(self, X)	Predict using the RVC model.
`predict_proba`(self, X)	Return an array of class probabilities.
`score`(self, X, y[, sample_weight])	Return the mean accuracy on the given test data and labels.
`set_params`(self, \\params)	Set the parameters of this estimator.

__init__(self, n_iter_posterior=50, kernel='rbf', degree=3, gamma='auto_deprecated', coef0=0.0, tol=0.001, threshold_alpha=1000000000.0, beta_fixed='not_fixed', alpha_max=10000000000.0, init_alpha=None, bias_used=True, max_iter=5000, compute_score=False, epsilon=1e-08, verbose=False)[source]¶: Initialize self. See help(type(self)) for accurate signature.

fit(self, X, y)[source]¶

Fit the RVC model according to the given training data.

Parameters

Xarray-like, shape (n_samples, n_features): Training vectors.
yarray-like, shape (n_samples,): Target values.

Returns

selfobject

get_params(self, 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

paramsmapping of string to any: Parameter names mapped to their values.

predict(self, X)[source]¶

Predict using the RVC model.

In addition to the mean of the predictive distribution, its standard deviation can also be returned.

Parameters

Xarray-like, shape (n_samples, n_features): Query points to be evaluate.

Returns

resultsarray, shape = (n_samples, [n_output_dims]): Mean of predictive distribution at query points

predict_proba(self, X)[source]¶: Return an array of class probabilities.

score(self, X, y, sample_weight=None)[source]¶

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) wrt. y.

set_params(self, **params)[source]¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). 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

selfobject: Estimator instance.

sklearn_rvm.em_rvm.EMRVC¶

`sklearn_rvm.em_rvm`.EMRVC¶