Python-package Introduction

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MVBLS.MVBLSClassifier

class MVBLS.MVBLSClassifier(n_nodes_H=1000, active_function='relu', n_nodes_Z=10, n_groups_Z=10, reg_alpha=0.1, reg_lambda=0.1, view_list=None, random_state=0)

Bases: sklearn.base.ClassifierMixin, MVBLS.MVBLS

MVBLS classifier. Construct a broad learning systerm model.

Parameters

• n_nodes_H (int, default=1000) – Controls the number of enhancement nodes.

• active_function ({str, ('relu', 'tanh', 'sigmoid' or 'linear')}, default='relu') – Controls the active function of enhancement nodes.

• n_nodes_Z (int, default=10) – Controls the number of feature nodes in each group.

• n_groups_Z (int, default=10) – Controls the number of feature node groups.

• reg_alpha (float, default=0.1) – Regularization strength; must be a positive float. Regularization improves the conditioning of the problem and reduces the variance of the estimates. Larger values specify stronger regularization.

• reg_lambda (float, default=0.1) – Constant that multiplies the L1 term. Defaults to 1.0. reg_lambda = 0 is equivalent to an ordinary least square.

• view_list (list, default=None) – List of view names.

• random_state (int, default=0) – Controls the randomness of the estimator.

property classes_

Classes labels

fit(X, y, sample_weight=None)

Build a broad learning systerm model from the training set (X, y).

Parameters

• X ({ndarray, sparse matrix} of shape (n_samples, n_features) or dict) – Training data.

• y (ndarray of shape (n_samples,)) – Target values.

• sample_weight (float or ndarray of shape (n_samples,), default=None) – Individual weights for each sample. If given a float, every sample will have the same weight.

Returns

self – Instance of the estimator.

Return type

object

get_params(deep=True)

Get parameters for this estimator.

Parameters

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

params – Parameter names mapped to their values.

Return type

dict

predict(X)

Predict class labels for samples in X.

Parameters

X (array_like or sparse matrix, shape (n_samples, n_features)) – Samples.

Returns

C – Predicted class label per sample.

Return type

array, shape [n_samples]

save_model(file)

Parameters

file (str) – Controls the filename.

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

• X (array-like of shape (n_samples, n_features)) – Test samples.

• y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True labels for X.

• sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.

Returns

score – Mean accuracy of self.predict(X) wrt. y.

Return type

float

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

**params (dict) – Estimator parameters.

Returns

self – Estimator instance.

Return type

estimator instance

MVBLS.MVBLSRegressor

class MVBLS.MVBLSRegressor(n_nodes_H=1000, active_function='relu', n_nodes_Z=10, n_groups_Z=10, reg_alpha=0.1, reg_lambda=0.1, view_list=None, random_state=0)

Bases: sklearn.base.MultiOutputMixin, sklearn.base.RegressorMixin, MVBLS.MVBLS

MVBLS Regressor. Construct a broad learning systerm model.

Parameters

• n_nodes_H (int, default=1000) – Controls the number of enhancement nodes.

• active_function ({str, ('relu', 'tanh', 'sigmoid' or 'linear')}, default='relu') – Controls the active function of enhancement nodes.

• n_nodes_Z (int, default=10) – Controls the number of feature nodes in each group.

• n_groups_Z (int, default=10) – Controls the number of feature node groups.

• reg_alpha (float, default=0.1) – Regularization strength; must be a positive float. Regularization improves the conditioning of the problem and reduces the variance of the estimates. Larger values specify stronger regularization.

• reg_lambda (float, default=0.1) – Constant that multiplies the L1 term. Defaults to 1.0. alpha = 0 is equivalent to an ordinary least square.

• view_list (list, default=None) – List of view names.

• random_state (int, default=0) – Controls the randomness of the estimator.

fit(X, y, sample_weight=None)

Build a broad learning systerm model from the training set (X, y).

Parameters

• X ({ndarray, sparse matrix} of shape (n_samples, n_features) or dict) – Training data

• y (ndarray of shape (n_samples,) or (n_samples, n_targets)) – Target values

Returns

self

Return type

returns an instance of self.

get_params(deep=True)

Get parameters for this estimator.

Parameters

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

params – Parameter names mapped to their values.

Return type

dict

predict(X)

Return the predicted value for each sample.

Parameters

X (array_like or sparse matrix, shape (n_samples, n_features)) – Samples.

Returns

C – Returns predicted values.

Return type

array, shape (n_samples,)

save_model(file)

Parameters

file (str) – Controls the filename.

score(X, y, sample_weight=None)

Return the coefficient of determination of the prediction.

The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)** 2).sum() and \(v\) is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.

Parameters

• X (array-like of shape (n_samples, n_features)) – Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.

• y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True values for X.

• sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.

Returns

score – \(R^2\) of self.predict(X) wrt. y.

Return type

float

Notes

The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score(). This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

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

**params (dict) – Estimator parameters.

Returns

self – Estimator instance.

Return type

estimator instance

MVBLS.SemiMVBLSClassifier

class MVBLS.SemiMVBLSClassifier(reg_laplacian=1.0, k_neighbors=5, sigma=1.0, unlabeled_data=None, **kwargs)

Bases: sklearn.base.ClassifierMixin, MVBLS.SemiMVBLS

Semi-supervised MVBLS classifier. Construct a broad learning systerm model.

Parameters

• reg_laplacian (float, default=1.0) – Constant that multiplies the laplacian term. Defaults to 1.0. reg_laplacian = 0 is equivalent to a Ridge regression.

• k_neighbors (int, default=5) – Number of neighbors to use when constructing the affinity matrix.

• sigma (float, default=1.0) – Kernel coefficient for RBF.

• unlabeled_data ({ndarray, sparse matrix} of shape (n_samples, n_features) or {dict}) – Unlabeled training data.

• n_nodes_H (int, default=1000) – Controls the number of enhancement nodes.

• active_function ({str, ('relu', 'tanh', 'sigmoid' or 'linear')}, default='relu') – Controls the active function of enhancement nodes.

• n_nodes_Z (int, default=10) – Controls the number of feature nodes in each group.

• n_groups_Z (int, default=10) – Controls the number of feature node groups.

• reg_alpha (float, default=0.1) – Regularization strength; must be a positive float. Regularization improves the conditioning of the problem and reduces the variance of the estimates. Larger values specify stronger regularization.

• reg_lambda (float, default=0.1) – Constant that multiplies the L1 term. Defaults to 1.0. reg_lambda = 0 is equivalent to an ordinary least square.

• view_list (list, default=None) – List of view names.

• random_state (int, default=0) – Controls the randomness of the estimator.

property classes_

Classes labels

fit(X, y, sample_weight=None)

Build a broad learning systerm model from the training set (X, y).

Parameters

• X ({ndarray, sparse matrix} of shape (n_samples, n_features) or dict) – Training data.

• y (ndarray of shape (n_samples,)) – Target values.

• sample_weight (float or ndarray of shape (n_samples,), default=None) – Individual weights for each sample. If given a float, every sample will have the same weight.

Returns

self – Instance of the estimator.

Return type

object

get_params(deep=True)

Get parameters for this estimator.

Parameters

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

params – Parameter names mapped to their values.

Return type

dict

predict(X)

Predict class labels for samples in X.

Parameters

X (array_like or sparse matrix, shape (n_samples, n_features)) – Samples.

Returns

C – Predicted class label per sample.

Return type

array, shape [n_samples]

save_model(file)

Parameters

file (str) – Controls the filename.

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

• X (array-like of shape (n_samples, n_features)) – Test samples.

• y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True labels for X.

• sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.

Returns

score – Mean accuracy of self.predict(X) wrt. y.

Return type

float

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

**params (dict) – Estimator parameters.

Returns

self – Estimator instance.

Return type

estimator instance

MVBLS.SemiMVBLSRegressor

class MVBLS.SemiMVBLSRegressor(reg_laplacian=1.0, k_neighbors=5, sigma=1.0, unlabeled_data=None, **kwargs)

Bases: sklearn.base.MultiOutputMixin, sklearn.base.RegressorMixin, MVBLS.SemiMVBLS

Semi-supervised MVBLS regressor. Construct a broad learning systerm model.

Parameters

• reg_laplacian (float, default=1.0) – Constant that multiplies the laplacian term. Defaults to 1.0. reg_laplacian = 0 is equivalent to a Ridge regression.

• k_neighbors (int, default=5) – Number of neighbors to use when constructing the affinity matrix.

• sigma (float, default=1.0) – Kernel coefficient for RBF.

• unlabeled_data ({ndarray, sparse matrix} of shape (n_samples, n_features) or {dict}) – Unlabeled training data.

• n_nodes_H (int, default=1000) – Controls the number of enhancement nodes.

• active_function ({str, ('relu', 'tanh', 'sigmoid' or 'linear')}, default='relu') – Controls the active function of enhancement nodes.

• n_nodes_Z (int, default=10) – Controls the number of feature nodes in each group.

• n_groups_Z (int, default=10) – Controls the number of feature node groups.

• reg_alpha (float, default=0.1) – Regularization strength; must be a positive float. Regularization improves the conditioning of the problem and reduces the variance of the estimates. Larger values specify stronger regularization.

• reg_lambda (float, default=0.1) – Constant that multiplies the L1 term. Defaults to 1.0. alpha = 0 is equivalent to an ordinary least square.

• view_list (list, default=None) – List of view names.

• random_state (int, default=0) – Controls the randomness of the estimator.

fit(X, y, sample_weight=None)

Build a broad learning systerm model from the training set (X, y).

Parameters

• X ({ndarray, sparse matrix} of shape (n_samples, n_features) or dict) – Training data

• y (ndarray of shape (n_samples,) or (n_samples, n_targets)) – Target values

Returns

self

Return type

returns an instance of self.

get_params(deep=True)

Get parameters for this estimator.

Parameters

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

params – Parameter names mapped to their values.

Return type

dict

predict(X)

Return the predicted value for each sample.

Parameters

X (array_like or sparse matrix, shape (n_samples, n_features)) – Samples.

Returns

C – Returns predicted values.

Return type

array, shape (n_samples,)

save_model(file)

Parameters

file (str) – Controls the filename.

score(X, y, sample_weight=None)

Return the coefficient of determination of the prediction.

The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)** 2).sum() and \(v\) is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.

Parameters

• X (array-like of shape (n_samples, n_features)) – Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.

• y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True values for X.

• sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.

Returns

score – \(R^2\) of self.predict(X) wrt. y.

Return type

float

Notes

The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score(). This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

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

**params (dict) – Estimator parameters.

Returns

self – Estimator instance.

Return type

estimator instance