IntelELM Library

intelelm.base_elm module

class intelelm.base_elm.BaseElm(hidden_size=10, act_name='elu')

Bases: sklearn.base.BaseEstimator

Defines the most general class for ELM network that inherits the BaseEstimator class of Scikit-Learn library.

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.

CLS_OBJ_LOSSES = None

SUPPORTED_CLS_METRICS = {'AS': 'max', 'BSL': 'min', 'CEL': 'min', 'CKS': 'max', 'F1S': 'max', 'F2S': 'max', 'FBS': 'max', 'GINI': 'min', 'GMS': 'max', 'HL': 'min', 'HS': 'max', 'JSI': 'max', 'KLDL': 'min', 'LS': 'max', 'MCC': 'max', 'NPV': 'max', 'PS': 'max', 'ROC-AUC': 'max', 'RS': 'max', 'SS': 'max'}

SUPPORTED_REG_METRICS = {'A10': 'max', 'A20': 'max', 'A30': 'max', 'ACOD': 'max', 'APCC': 'max', 'AR': 'max', 'AR2': 'max', 'CI': 'max', 'COD': 'max', 'COR': 'max', 'COV': 'max', 'CRM': 'min', 'DRV': 'min', 'EC': 'max', 'EVS': 'max', 'GINI': 'min', 'GINI_WIKI': 'min', 'JSD': 'min', 'KGE': 'max', 'MAAPE': 'min', 'MAE': 'min', 'MAPE': 'min', 'MASE': 'min', 'ME': 'min', 'MRB': 'min', 'MRE': 'min', 'MSE': 'min', 'MSLE': 'min', 'MedAE': 'min', 'NNSE': 'max', 'NRMSE': 'min', 'NSE': 'max', 'OI': 'max', 'PCC': 'max', 'PCD': 'max', 'R': 'max', 'R2': 'max', 'R2S': 'max', 'RAE': 'min', 'RMSE': 'min', 'RSE': 'min', 'RSQ': 'max', 'SMAPE': 'min', 'VAF': 'max', 'WI': 'max'}

create_network(X, y)

evaluate(y_true, y_pred, list_metrics=None)

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) – You can get metrics from Permetrics library: https://github.com/thieu1995/permetrics

Returns

results – The results of the list metrics

Return type

dict

fit(X, y)

static load_model(load_path='history', filename='model.pkl')

predict(X, return_prob=False)

Inherit the predict function from BaseElm class, with 1 more parameter return_prob.

Parameters

• X ({array-like, sparse matrix} of shape (n_samples, n_features)) – The input data.

• return_prob (bool, default=False) –

  It is used for classification problem:

  • If True, the returned results are the probability for each sample

  • If False, the returned results are the predicted labels

save_loss_train(save_path='history', filename='loss.csv')

Save the loss (convergence) during the training process to csv file.

Parameters

• save_path (saved path (relative path, consider from current executed script path)) –

• filename (name of the file, needs to have ".csv" extension) –

save_metrics(y_true, y_pred, list_metrics=('RMSE', 'MAE'), save_path='history', filename='metrics.csv')

Save evaluation metrics to csv file

Parameters

• y_true (ground truth data) –

• y_pred (predicted output) –

• list_metrics (list of evaluation metrics) –

• save_path (saved path (relative path, consider from current executed script path)) –

• filename (name of the file, needs to have ".csv" extension) –

save_model(save_path='history', filename='model.pkl')

Save model to pickle file

Parameters

• save_path (saved path (relative path, consider from current executed script path)) –

• filename (name of the file, needs to have ".pkl" extension) –

save_y_predicted(X, y_true, save_path='history', filename='y_predicted.csv')

Save the predicted results to csv file

Parameters

• X (The features data, nd.ndarray) –

• y_true (The ground truth data) –

• save_path (saved path (relative path, consider from current executed script path)) –

• filename (name of the file, needs to have ".csv" extension) –

score(X, y, method=None)

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 metrics from Permetrics library: https://github.com/thieu1995/permetrics

Returns

result – The result of selected metric

Return type

float

scores(X, y, list_methods=None)

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 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.base_elm.BaseElm

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.base_elm.BaseElm

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.base_elm.BaseMhaElm(hidden_size=10, act_name='elu', obj_name=None, optimizer='BaseGA', optimizer_paras=None, verbose=True, seed=None)

Bases: intelelm.base_elm.BaseElm

Defines the most general class for Metaheuristic-based ELM model that inherits the BaseELM class

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=True) – 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.

SUPPORTED_CLS_OBJECTIVES = {'AS': 'max', 'BSL': 'min', 'CEL': 'min', 'CKS': 'max', 'F1S': 'max', 'F2S': 'max', 'FBS': 'max', 'GINI': 'min', 'GMS': 'max', 'HL': 'min', 'HS': 'max', 'JSI': 'max', 'KLDL': 'min', 'LS': 'max', 'MCC': 'max', 'NPV': 'max', 'PS': 'max', 'ROC-AUC': 'max', 'RS': 'max', 'SS': 'max'}

SUPPORTED_OPTIMIZERS = ['OriginalABC', 'OriginalACOR', 'AugmentedAEO', 'EnhancedAEO', 'ImprovedAEO', 'ModifiedAEO', 'OriginalAEO', 'MGTO', 'OriginalAGTO', 'DevALO', 'OriginalALO', 'OriginalAO', 'OriginalAOA', 'IARO', 'LARO', 'OriginalARO', 'OriginalASO', 'OriginalAVOA', 'OriginalArchOA', 'AdaptiveBA', 'DevBA', 'OriginalBA', 'DevBBO', 'OriginalBBO', 'OriginalBBOA', 'OriginalBES', 'ABFO', 'OriginalBFO', 'OriginalBMO', 'DevBRO', 'OriginalBRO', 'OriginalBSA', 'ImprovedBSO', 'OriginalBSO', 'CleverBookBeesA', 'OriginalBeesA', 'ProbBeesA', 'OriginalCA', 'OriginalCDO', 'OriginalCEM', 'OriginalCGO', 'DevCHIO', 'OriginalCHIO', 'OriginalCOA', 'OCRO', 'OriginalCRO', 'OriginalCSA', 'OriginalCSO', 'OriginalCircleSA', 'OriginalCoatiOA', 'JADE', 'OriginalDE', 'SADE', 'SAP_DE', 'DevDMOA', 'OriginalDMOA', 'OriginalDO', 'DevEFO', 'OriginalEFO', 'OriginalEHO', 'AdaptiveEO', 'ModifiedEO', 'OriginalEO', 'OriginalEOA', 'LevyEP', 'OriginalEP', 'CMA_ES', 'LevyES', 'OriginalES', 'Simple_CMA_ES', 'OriginalESOA', 'OriginalEVO', 'OriginalFA', 'DevFBIO', 'OriginalFBIO', 'OriginalFFA', 'OriginalFFO', 'OriginalFLA', 'DevFOA', 'OriginalFOA', 'WhaleFOA', 'OriginalFOX', 'OriginalFPA', 'BaseGA', 'EliteMultiGA', 'EliteSingleGA', 'MultiGA', 'SingleGA', 'OriginalGBO', 'DevGCO', 'OriginalGCO', 'OriginalGJO', 'OriginalGOA', 'DevGSKA', 'OriginalGSKA', 'Matlab101GTO', 'Matlab102GTO', 'OriginalGTO', 'GWO_WOA', 'IGWO', 'OriginalGWO', 'RW_GWO', 'OriginalHBA', 'OriginalHBO', 'OriginalHC', 'SwarmHC', 'OriginalHCO', 'OriginalHGS', 'OriginalHGSO', 'OriginalHHO', 'DevHS', 'OriginalHS', 'OriginalICA', 'OriginalINFO', 'OriginalIWO', 'DevJA', 'LevyJA', 'OriginalJA', 'DevLCO', 'ImprovedLCO', 'OriginalLCO', 'OriginalMA', 'OriginalMFO', 'OriginalMGO', 'OriginalMPA', 'OriginalMRFO', 'WMQIMRFO', 'OriginalMSA', 'DevMVO', 'OriginalMVO', 'OriginalNGO', 'ImprovedNMRA', 'OriginalNMRA', 'OriginalNRO', 'OriginalOOA', 'OriginalPFA', 'OriginalPOA', 'AIW_PSO', 'CL_PSO', 'C_PSO', 'HPSO_TVAC', 'LDW_PSO', 'OriginalPSO', 'P_PSO', 'OriginalPSS', 'DevQSA', 'ImprovedQSA', 'LevyQSA', 'OppoQSA', 'OriginalQSA', 'OriginalRIME', 'OriginalRUN', 'GaussianSA', 'OriginalSA', 'SwarmSA', 'DevSARO', 'OriginalSARO', 'DevSBO', 'OriginalSBO', 'DevSCA', 'OriginalSCA', 'QleSCA', 'OriginalSCSO', 'ImprovedSFO', 'OriginalSFO', 'L_SHADE', 'OriginalSHADE', 'OriginalSHIO', 'OriginalSHO', 'ImprovedSLO', 'ModifiedSLO', 'OriginalSLO', 'DevSMA', 'OriginalSMA', 'DevSOA', 'OriginalSOA', 'OriginalSOS', 'DevSPBO', 'OriginalSPBO', 'OriginalSRSR', 'DevSSA', 'OriginalSSA', 'OriginalSSDO', 'OriginalSSO', 'OriginalSSpiderA', 'OriginalSSpiderO', 'OriginalSTO', 'OriginalSeaHO', 'OriginalServalOA', 'OriginalTDO', 'DevTLO', 'ImprovedTLO', 'OriginalTLO', 'OriginalTOA', 'DevTPO', 'OriginalTS', 'OriginalTSA', 'OriginalTSO', 'EnhancedTWO', 'LevyTWO', 'OppoTWO', 'OriginalTWO', 'DevVCS', 'OriginalVCS', 'OriginalWCA', 'OriginalWDO', 'OriginalWHO', 'HI_WOA', 'OriginalWOA', 'OriginalWaOA', 'OriginalWarSO', 'OriginalZOA']

SUPPORTED_REG_OBJECTIVES = {'A10': 'max', 'A20': 'max', 'A30': 'max', 'ACOD': 'max', 'APCC': 'max', 'AR': 'max', 'AR2': 'max', 'CI': 'max', 'COD': 'max', 'COR': 'max', 'COV': 'max', 'CRM': 'min', 'DRV': 'min', 'EC': 'max', 'EVS': 'max', 'GINI': 'min', 'GINI_WIKI': 'min', 'JSD': 'min', 'KGE': 'max', 'MAAPE': 'min', 'MAE': 'min', 'MAPE': 'min', 'MASE': 'min', 'ME': 'min', 'MRB': 'min', 'MRE': 'min', 'MSE': 'min', 'MSLE': 'min', 'MedAE': 'min', 'NNSE': 'max', 'NRMSE': 'min', 'NSE': 'max', 'OI': 'max', 'PCC': 'max', 'PCD': 'max', 'R': 'max', 'R2': 'max', 'R2S': 'max', 'RAE': 'min', 'RMSE': 'min', 'RSE': 'min', 'RSQ': 'max', 'SMAPE': 'min', 'VAF': 'max', 'WI': 'max'}

fit(X, y, lb=(- 10.0,), ub=(10.0,), mode='single', n_workers=None, termination=None, save_population=False)

Parameters

• X (The features data, np.ndarray) –

• y (The ground truth data) –

• lb (The lower bound for decision variables in optimization problem (The weights and biases of network)) –

• ub (The upper bound for decision variables in optimization problem (The weights and biases of network)) –

• mode (Parallel: 'process', 'thread'; Sequential: 'swarm', 'single'.) –

  • ‘process’: The parallel mode with multiple cores run the tasks

  • ’thread’: The parallel mode with multiple threads run the tasks

  • ’swarm’: The sequential mode that no effect on updating phase of other agents

  • ’single’: The sequential mode that effect on updating phase of other agents, this is default mode

• n_workers (The number of workers (cores or threads) to do the tasks (effect only on parallel mode)) –

• termination (The termination dictionary or an instance of Termination class in Mealpy library) –

• save_population (Save the population of search agents (Don't set it to True when you don't know how to use it)) –

fitness_function(solution=None)

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.base_elm.BaseMhaElm

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.base_elm.BaseMhaElm

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.base_elm.BaseMhaElm

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.base_elm.ELM(size_input=5, size_hidden=10, size_output=1, act_name='sigmoid', seed=None)

Bases: object

Extreme Learning Machine

This class defines the general ELM model

Parameters

• size_input (int, default=5) – The number of input nodes

• size_hidden (int, default=10) – The number of hidden nodes

• size_output (int, default=1) – The number of output 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.

fit(X, y)

Fit the model to data matrix X and target(s) y.

Parameters

• X (ndarray or sparse matrix of shape (n_samples, n_features)) – The input data.

• y (ndarray of shape (n_samples,) or (n_samples, n_outputs)) – The target values (class labels in classification, real numbers in regression).

Returns

self – Returns a trained ELM model.

Return type

object

get_weights()

get_weights_size()

predict(X)

Predict using the Extreme Learning Machine model.

Parameters

X ({array-like, sparse matrix} of shape (n_samples, n_features)) – The input data.

Returns

y – The predicted values.

Return type

ndarray of shape (n_samples, n_outputs)

set_weights(weights)

update_weights_from_solution(solution, X, y)

Subpackages

• intelelm.model package
  • intelelm.model.standard_elm module
  • intelelm.model.mha_elm module
• intelelm.utils package
  • intelelm.utils.activation module
  • intelelm.utils.data_loader module
  • intelelm.utils.encoder module
  • intelelm.utils.evaluator module
  • intelelm.utils.validator module