skelm.ELMClassifier

class skelm.ELMClassifier(classes=None, alpha=1e-07, batch_size=None, include_original_features=False, n_neurons=None, ufunc='tanh', density=None, pairwise_metric=None, random_state=None)[source]

ELM classifier, modified for multi-label classification support.

Parameters:

  • classes – Set of classes to consider in the model; can be expanded at runtime. Samples of other classes will have their output set to zero.

  • solver – Solver to use, “default” for build-in Least Squares or “ridge” for Ridge regression

Example descr…

X_

The input passed during fit().

Type:

ndarray, shape (n_samples, n_features)

y_

The labels passed during fit().

Type:

ndarray, shape (n_samples,)

classes_

The classes seen at fit().

Type:

ndarray, shape (n_classes,)

__init__(classes=None, alpha=1e-07, batch_size=None, include_original_features=False, n_neurons=None, ufunc='tanh', density=None, pairwise_metric=None, random_state=None)[source]

Scikit-ELM’s version of __init__, that only saves input parameters and does nothing else.

fit(X, y=None) → ELMClassifier[source]

Fit a classifier erasing any previously trained model.

Returns:

self – Returns 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

partial_fit(X, y=None, forget=False, update_classes=False, compute_output_weights=True) → ELMClassifier[source]

Update classifier with a new batch of data.

|method_partial_fit|

Parameters:

  • X ({array-like, sparse matrix}, shape=[n_samples, n_features]) – Training input samples

  • y (array-like, shape=[n_samples, n_targets]) – Training targets

  • forget (boolean, default False) – |param_forget|

  • update_classes (boolean, default False) – Include new classes from y into the model, assuming they were 0 in all previous samples.

  • compute_output_weights (boolean, optional, default True) – |param_compute_output_weights|

predict(X) → _SupportsArray[dtype] | _NestedSequence[_SupportsArray[dtype]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes][source]

Predict classes of new inputs X.

Parameters:

X (array-like, shape (n_samples, n_features)) – The input samples.

Returns:

y – Returns one most probable class for multi-class problem, or a binary vector of all relevant classes for multi-label problem.

Return type:

ndarray, shape (n_samples,) or (n_samples, n_outputs)

predict_proba(X) → _SupportsArray[dtype] | _NestedSequence[_SupportsArray[dtype]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes][source]

Probability estimation for all classes.

Positive class probabilities are computed as 1. / (1. + np.exp(-self.decision_function(X))); multiclass is handled by normalizing that over all classes.

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) w.r.t. 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

Examples using skelm.ELMClassifier

Plotting Template Classifier

Plotting Template Classifier