intelelm.model package¶

intelelm.model.standard_elm module¶

class intelelm.model.standard_elm.ElmClassifier(hidden_size=10, act_name='elu', seed=None)[source]¶

Bases: intelelm.base_elm.BaseElm, sklearn.base.ClassifierMixin

Defines the general class of Metaheuristic-based ELM model for Classification problems that inherit the BaseElm and ClassifierMixin classes.

Parameters

hidden_size (int, default=10) – The number of hidden nodes
act_name ({"relu", "leaky_relu", "celu", "prelu", "gelu", "elu", "selu", "rrelu", "tanh", "hard_tanh", "sigmoid",) – “hard_sigmoid”, “log_sigmoid”, “silu”, “swish”, “hard_swish”, “soft_plus”, “mish”, “soft_sign”, “tanh_shrink”, “soft_shrink”, “hard_shrink”, “softmin”, “softmax”, “log_softmax” }, default=’sigmoid’ Activation function for the hidden layer.
seed (int, default=None) – Determines random number generation for weights and bias initialization. Pass an int for reproducible results across multiple function calls.

Examples

>>> from intelelm import Data, ElmClassifier
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_samples=100, random_state=1)
>>> data = Data(X, y)
>>> data.split_train_test(test_size=0.2, random_state=1)
>>> model = ElmClassifier(hidden_size=10, act_name="elu")
>>> model.fit(data.X_train, data.y_train)
>>> pred = model.predict(data.X_test)
>>> print(pred)
array([1, 0, 1, 0, 1])

CLS_OBJ_LOSSES = ['CEL', 'HL', 'KLDL', 'BSL']¶

create_network(X, y)[source]¶

evaluate(y_true, y_pred, list_metrics=('AS', 'RS'))[source]¶

Return the list of performance metrics on the given test data and labels.

Parameters

y_true (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True values for X.
y_pred (array-like of shape (n_samples,) or (n_samples, n_outputs)) – Predicted values for X.
list_metrics (list, default=("AS", "RS")) – You can get metrics from Permetrics library: https://github.com/thieu1995/permetrics

Returns

results – The results of the list metrics

Return type

dict

score(X, y, method='AS')[source]¶

Return the metric 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.
method (str, default="AS") – You can get all of the metrics from Permetrics library: https://github.com/thieu1995/permetrics

Returns

result – The result of selected metric

Return type

float

scores(X, y, list_methods=('AS', 'RS'))[source]¶

Return the list of metrics 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.
list_methods (list, default=("AS", "RS")) – You can get all of the metrics from Permetrics library: https://github.com/thieu1995/permetrics

Returns

results – The results of the list metrics

Return type

dict

set_predict_request(*, return_prob: Union[bool, None, str] = '$UNCHANGED$') → intelelm.model.standard_elm.ElmClassifier¶

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.

New 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: return_prob (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for return_prob parameter in predict.
Returns: self – The updated object.
Return type: object

set_score_request(*, method: Union[bool, None, str] = '$UNCHANGED$') → intelelm.model.standard_elm.ElmClassifier¶

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.

New 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: method (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for method parameter in score.
Returns: self – The updated object.
Return type: object

class intelelm.model.standard_elm.ElmRegressor(hidden_size=10, act_name='elu', seed=None)[source]¶

Bases: intelelm.base_elm.BaseElm, sklearn.base.RegressorMixin

Defines the ELM model for Regression problems that inherit the BaseElm and RegressorMixin classes.

Parameters

hidden_size (int, default=10) – The number of hidden nodes
act_name ({"relu", "leaky_relu", "celu", "prelu", "gelu", "elu", "selu", "rrelu", "tanh", "hard_tanh", "sigmoid",) – “hard_sigmoid”, “log_sigmoid”, “silu”, “swish”, “hard_swish”, “soft_plus”, “mish”, “soft_sign”, “tanh_shrink”, “soft_shrink”, “hard_shrink”, “softmin”, “softmax”, “log_softmax” }, default=’sigmoid’ Activation function for the hidden layer.
seed (int, default=None) – Determines random number generation for weights and bias initialization. Pass an int for reproducible results across multiple function calls.

Examples

>>> from intelelm import ElmRegressor, Data
>>> from sklearn.datasets import make_regression
>>> X, y = make_regression(n_samples=200, random_state=1)
>>> data = Data(X, y)
>>> data.split_train_test(test_size=0.2, random_state=1)
>>> model = ElmRegressor(hidden_size=10, act_name="elu")
>>> model.fit(data.X_train, data.y_train)
>>> pred = model.predict(data.X_test)
>>> print(pred)

create_network(X, y)[source]¶

evaluate(y_true, y_pred, list_metrics=('MSE', 'MAE'))[source]¶

Return the list of performance metrics of the prediction.

Parameters

y_true (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True values for X.
y_pred (array-like of shape (n_samples,) or (n_samples, n_outputs)) – Predicted values for X.
list_metrics (list, default=("MSE", "MAE")) – You can get metrics from Permetrics library: https://github.com/thieu1995/permetrics

Returns

results – The results of the list metrics

Return type

dict

score(X, y, method='RMSE')[source]¶

Return the metric of the prediction.

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.
method (str, default="RMSE") – You can get all of the metrics from Permetrics library: https://github.com/thieu1995/permetrics

Returns

result – The result of selected metric

Return type

float

scores(X, y, list_methods=('MSE', 'MAE'))[source]¶

Return the list of metrics of the prediction.

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.
list_methods (list, default=("MSE", "MAE")) – You can get all of the metrics from Permetrics library: https://github.com/thieu1995/permetrics

Returns

results – The results of the list metrics

Return type

dict

set_predict_request(*, return_prob: Union[bool, None, str] = '$UNCHANGED$') → intelelm.model.standard_elm.ElmRegressor¶

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.

New 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: return_prob (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for return_prob parameter in predict.
Returns: self – The updated object.
Return type: object

set_score_request(*, method: Union[bool, None, str] = '$UNCHANGED$') → intelelm.model.standard_elm.ElmRegressor¶

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.

New 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: method (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for method parameter in score.
Returns: self – The updated object.
Return type: object

intelelm.model.mha_elm module¶

class intelelm.model.mha_elm.MhaElmClassifier(hidden_size=10, act_name='elu', obj_name=None, optimizer='BaseGA', optimizer_paras=None, verbose=False, seed=None)[source]¶

Bases: intelelm.base_elm.BaseMhaElm, sklearn.base.ClassifierMixin

Defines the general class of Metaheuristic-based ELM model for Classification problems that inherit the BaseMhaElm and ClassifierMixin classes.

Parameters

hidden_size (int, default=10) – The number of hidden nodes
act_name ({"relu", "leaky_relu", "celu", "prelu", "gelu", "elu", "selu", "rrelu", "tanh", "hard_tanh", "sigmoid",) – “hard_sigmoid”, “log_sigmoid”, “silu”, “swish”, “hard_swish”, “soft_plus”, “mish”, “soft_sign”, “tanh_shrink”, “soft_shrink”, “hard_shrink”, “softmin”, “softmax”, “log_softmax” }, default=’sigmoid’ Activation function for the hidden layer.
obj_name (None or str, default=None) – The name of objective for the problem, also depend on the problem is classification and regression.
optimizer (str or instance of Optimizer class (from Mealpy library), default = "BaseGA") – The Metaheuristic Algorithm that use to solve the feature selection problem. Current supported list, please check it here: https://github.com/thieu1995/mealpy. If a custom optimizer is passed, make sure it is an instance of Optimizer class.
optimizer_paras (None or dict of parameter, default=None) – The parameter for the optimizer object. If None, the default parameters of optimizer is used (defined in https://github.com/thieu1995/mealpy.) If dict is passed, make sure it has at least epoch and pop_size parameters.
verbose (bool, default=False) – Whether to print progress messages to stdout.
seed (int, default=None) – Determines random number generation for weights and bias initialization. Pass an int for reproducible results across multiple function calls.

Examples

>>> from intelelm import Data, MhaElmClassifier
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_samples=100, random_state=1)
>>> data = Data(X, y)
>>> data.split_train_test(test_size=0.2, random_state=1)
>>> opt_paras = {"name": "GA", "epoch": 10, "pop_size": 30}
>>> print(MhaElmClassifier.SUPPORTED_CLS_OBJECTIVES)
{'PS': 'max', 'NPV': 'max', 'RS': 'max', ...., 'KLDL': 'min', 'BSL': 'min'}
>>> model = MhaElmClassifier(hidden_size=10, act_name="elu", obj_name="BSL", optimizer="BaseGA", optimizer_paras=opt_paras)
>>> model.fit(data.X_train, data.y_train)
>>> pred = model.predict(data.X_test)
>>> print(pred)
array([1, 0, 1, 0, 1])

CLS_OBJ_LOSSES = ['CEL', 'HL', 'KLDL', 'BSL']¶

create_network(X, y)[source]¶

evaluate(y_true, y_pred, list_metrics=('AS', 'RS'))[source]¶

Return the list of performance metrics on the given test data and labels.

Parameters

y_true (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True values for X.
y_pred (array-like of shape (n_samples,) or (n_samples, n_outputs)) – Predicted values for X.
list_metrics (list, default=("AS", "RS")) – You can get metrics from Permetrics library: https://github.com/thieu1995/permetrics

Returns

results – The results of the list metrics

Return type

dict

fitness_function(solution=None)[source]¶

Evaluates the fitness function for classification metric

Parameters: solution (np.ndarray, default=None) –
Returns: result – The fitness value
Return type: float

score(X, y, method='AS')[source]¶

Return the metric 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.
method (str, default="AS") – You can get all of the metrics from Permetrics library: https://github.com/thieu1995/permetrics

Returns

result – The result of selected metric

Return type

float

scores(X, y, list_methods=('AS', 'RS'))[source]¶

Return the list of metrics 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.
list_methods (list, default=("AS", "RS")) – You can get all of the metrics from Permetrics library: https://github.com/thieu1995/permetrics

Returns

results – The results of the list metrics

Return type

dict

set_fit_request(*, lb: Union[bool, None, str] = '$UNCHANGED$', mode: Union[bool, None, str] = '$UNCHANGED$', n_workers: Union[bool, None, str] = '$UNCHANGED$', save_population: Union[bool, None, str] = '$UNCHANGED$', termination: Union[bool, None, str] = '$UNCHANGED$', ub: Union[bool, None, str] = '$UNCHANGED$') → intelelm.model.mha_elm.MhaElmClassifier¶

Request metadata passed to the fit 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 fit 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 fit.
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.

New 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

lb (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for lb parameter in fit.
mode (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for mode parameter in fit.
n_workers (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for n_workers parameter in fit.
save_population (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for save_population parameter in fit.
termination (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for termination parameter in fit.
ub (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for ub parameter in fit.

Returns

self – The updated object.

Return type

object

set_predict_request(*, return_prob: Union[bool, None, str] = '$UNCHANGED$') → intelelm.model.mha_elm.MhaElmClassifier¶

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.

New 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: return_prob (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for return_prob parameter in predict.
Returns: self – The updated object.
Return type: object

set_score_request(*, method: Union[bool, None, str] = '$UNCHANGED$') → intelelm.model.mha_elm.MhaElmClassifier¶

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.

New 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: method (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for method parameter in score.
Returns: self – The updated object.
Return type: object

class intelelm.model.mha_elm.MhaElmRegressor(hidden_size=10, act_name='elu', obj_name=None, optimizer='BaseGA', optimizer_paras=None, verbose=False, seed=None, obj_weights=None)[source]¶

Bases: intelelm.base_elm.BaseMhaElm, sklearn.base.RegressorMixin

Defines the general class of Metaheuristic-based ELM model for Regression problems that inherit the BaseMhaElm and RegressorMixin classes.

Parameters

hidden_size (int, default=10) – The number of hidden nodes
act_name ({"relu", "leaky_relu", "celu", "prelu", "gelu", "elu", "selu", "rrelu", "tanh", "hard_tanh", "sigmoid",) – “hard_sigmoid”, “log_sigmoid”, “silu”, “swish”, “hard_swish”, “soft_plus”, “mish”, “soft_sign”, “tanh_shrink”, “soft_shrink”, “hard_shrink”, “softmin”, “softmax”, “log_softmax” }, default=’sigmoid’ Activation function for the hidden layer.
obj_name (None or str, default=None) – The name of objective for the problem, also depend on the problem is classification and regression.
optimizer (str or instance of Optimizer class (from Mealpy library), default = "BaseGA") – The Metaheuristic Algorithm that use to solve the feature selection problem. Current supported list, please check it here: https://github.com/thieu1995/mealpy. If a custom optimizer is passed, make sure it is an instance of Optimizer class.
optimizer_paras (None or dict of parameter, default=None) – The parameter for the optimizer object. If None, the default parameters of optimizer is used (defined in https://github.com/thieu1995/mealpy.) If dict is passed, make sure it has at least epoch and pop_size parameters.
verbose (bool, default=False) – Whether to print progress messages to stdout.
seed (int, default=None) – Determines random number generation for weights and bias initialization. Pass an int for reproducible results across multiple function calls.

Examples

>>> from intelelm import MhaElmRegressor, Data
>>> from sklearn.datasets import make_regression
>>> X, y = make_regression(n_samples=200, random_state=1)
>>> data = Data(X, y)
>>> data.split_train_test(test_size=0.2, random_state=1)
>>> opt_paras = {"name": "GA", "epoch": 10, "pop_size": 30}
>>> model = MhaElmRegressor(hidden_size=10, act_name="elu", obj_name="RMSE", optimizer="BaseGA", optimizer_paras=opt_paras)
>>> model.fit(data.X_train, data.y_train)
>>> pred = model.predict(data.X_test)
>>> print(pred)

create_network(X, y)[source]¶

evaluate(y_true, y_pred, list_metrics=('MSE', 'MAE'))[source]¶

Return the list of performance metrics of the prediction.

Parameters

y_true (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True values for X.
y_pred (array-like of shape (n_samples,) or (n_samples, n_outputs)) – Predicted values for X.
list_metrics (list, default=("MSE", "MAE")) – You can get metrics from Permetrics library: https://github.com/thieu1995/permetrics

Returns

results – The results of the list metrics

Return type

dict

fitness_function(solution=None)[source]¶

Evaluates the fitness function for regression metric

Parameters: solution (np.ndarray, default=None) –
Returns: result – The fitness value
Return type: float

score(X, y, method='RMSE')[source]¶

Return the metric of the prediction.

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.
method (str, default="RMSE") – You can get all of the metrics from Permetrics library: https://github.com/thieu1995/permetrics

Returns

result – The result of selected metric

Return type

float

scores(X, y, list_methods=('MSE', 'MAE'))[source]¶

Return the list of metrics of the prediction.

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.
list_methods (list, default=("MSE", "MAE")) – You can get all of the metrics from Permetrics library: https://github.com/thieu1995/permetrics

Returns

results – The results of the list metrics

Return type

dict

set_fit_request(*, lb: Union[bool, None, str] = '$UNCHANGED$', mode: Union[bool, None, str] = '$UNCHANGED$', n_workers: Union[bool, None, str] = '$UNCHANGED$', save_population: Union[bool, None, str] = '$UNCHANGED$', termination: Union[bool, None, str] = '$UNCHANGED$', ub: Union[bool, None, str] = '$UNCHANGED$') → intelelm.model.mha_elm.MhaElmRegressor¶

Request metadata passed to the fit 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 fit 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 fit.
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.

New 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

lb (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for lb parameter in fit.
mode (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for mode parameter in fit.
n_workers (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for n_workers parameter in fit.
save_population (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for save_population parameter in fit.
termination (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for termination parameter in fit.
ub (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for ub parameter in fit.

Returns

self – The updated object.

Return type

object

set_predict_request(*, return_prob: Union[bool, None, str] = '$UNCHANGED$') → intelelm.model.mha_elm.MhaElmRegressor¶

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.

New 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: return_prob (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for return_prob parameter in predict.
Returns: self – The updated object.
Return type: object

set_score_request(*, method: Union[bool, None, str] = '$UNCHANGED$') → intelelm.model.mha_elm.MhaElmRegressor¶

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.

New 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: method (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for method parameter in score.
Returns: self – The updated object.
Return type: object