pyoselm.elm.GenELMRegressor

class pyoselm.elm.GenELMRegressor(hidden_layer=None, regressor=None)

Regression model based on Extreme Learning Machine.

Parameters

• hidden_layer (random_layer instance, optional) – (default=MLPRandomLayer(random_state=0))

• regressor (regressor instance, optional) – (default=sklearn.linear_model.LinearRegression())

`coefs_`

Fitted regression coefficients if no regressor supplied.

Type

numpy array

`fitted_`

Flag set when fit has been called already.

Type

bool

`hidden_activations_`

Hidden layer activations for last input.

Type

numpy array of shape [n_samples, n_hidden]

See also

ELMRegressor, MLPRandomLayer

__init__(hidden_layer=None, regressor=None)

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__([hidden_layer, regressor])	Initialize self.
fit(X, y)	Fit the model using X, y as training data.
get_params([deep])	Get parameters for this estimator.
predict(X)	Predict values using the model
score(X, y[, sample_weight])	Return the coefficient of determination \(R^2\) of the prediction.
set_params(**params)	Set the parameters of this estimator.

Attributes

is_fitted

Check if model was fitted

fit(X, y)

Fit the model using X, y as training data.

Parameters

• X ({array-like, sparse matrix} of shape [n_samples, n_features]) – Training vectors, where n_samples is the number of samples and n_features is the number of features.

• y (array-like of shape [n_samples, n_outputs]) – Target values (class labels in classification, real numbers in regression)

Returns

self – Returns an instance of self.

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

property is_fitted

Check if model was fitted

Returns

Return type

boolean, True if model is fitted

predict(X)

Predict values using the model

Parameters

X ({array-like, sparse matrix} of shape [n_samples, n_features]) –

Returns

C – Predicted values.

Return type

numpy array of shape [n_samples, n_outputs]

score(X, y, sample_weight=None)

Return the coefficient of determination \(R^2\) of the prediction.

The coefficient \(R^2\) 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