intelelm.model package¶

intelelm.model.automated_comparator module¶

class intelelm.model.automated_comparator.AutomatedMhaElmComparator(optim_dict=None, task='classification', layer_sizes=(10,), act_name='elu', obj_name=None, verbose=False, seed=None, obj_weights=None, lb=None, ub=None, mode='single', n_workers=None, termination=None)[source]¶

Bases: object

A utility class for comparing different MhaElm models based on optimizer configurations. It supports cross-validation and train-test split evaluation for both classification and regression tasks.

optim_dict¶

Dictionary containing optimizer names and their parameters.

Type: dict

task¶

Type of task, either ‘classification’ or ‘regression’.

Type: str

layer_sizes¶

Number of nodes in each hidden layer.

Type: int, list, or tuple

act_name¶

Name of the activation function.

Type: str

obj_name¶

Name of the objective function.

Type: str

verbose¶

Whether to print verbose output.

Type: bool

seed¶

Random seed for reproducibility.

Type: int

obj_weights¶

Weights for the objective function in regression tasks.

Type: array-like, optional

lb¶

Lower bounds for optimization variables.

Type: list, tuple, np.ndarray, int, or float

ub¶

Upper bounds for optimization variables.

Type: list, tuple, np.ndarray, int, or float

mode¶

Optimization mode, e.g., ‘single’ or ‘swarm’, ‘thread, or ‘process’.

Type: str

n_workers¶

Number of workers for parallel optimization.

Type: int

termination¶

Termination criteria for the optimization process.

Type: object

models¶

List of MhaElmClassifier or MhaElmRegressor models.

Type: list

best_estimator_¶

Best model after comparison.

Type: object

best_params_¶

Parameters of the best model.

Type: dict

result_cross_val_scores_¶

Results from cross-validation comparison.

Type: dict

result_train_test_¶

Results from train-test split comparison.

Type: dict

__init__(optim_dict=None, task="classification", layer_sizes=(10,), act_name="elu", obj_name=None, verbose=False,: seed=None, obj_weights=None, lb=None, ub=None, mode=’single’, n_workers=None, termination=None)

Initializes the AutomatedMhaElmComparator with specified parameters.

_set_optimizer_dict(opt_dict)[source]¶: Validates and sets the optimizer dictionary.

_filter_metric_results(results, metric_names=None, return_train_score=False)[source]¶: Filters and formats metric results.

_rename_metrics(results, suffix='train')[source]¶: Renames metric keys with a specified suffix.

_results_to_csv(to_csv=False, results=None, saved_file_path='history/results.csv')[source]¶: Saves results to a CSV file.

compare_cross_validate(X, y, metrics=None, cv=5, return_train_score=True, n_trials=10, to_csv=True,: saved_file_path=”history/results_cross_validate.csv”, **kwargs)

Compares models using cross-validation with multiple metrics.

compare_cross_val_score(X, y, metric=None, cv=5, n_trials=10, to_csv=True,: saved_file_path=”history/results_cross_val_score.csv”, **kwargs)

Compares models using cross-validation with a single metric.

compare_train_test(X_train, y_train, X_test, y_test, metrics=None, n_trials=10, to_csv=True,: saved_file_path=”history/results_train_test.csv”)

Compares models using train-test split evaluation.

compare_cross_val_score(X, y, metric=None, cv=5, n_trials=10, to_csv=True, saved_file_path='history/results_cross_val_score.csv', **kwargs)[source]¶

Performs cross-validation with a single metric.

Compares different MhaElm models using cross-validation with a single metric.

Parameters

X (array-like) – The feature matrix.
y (array-like) – The target vector.
metric (str, optional) – The metric to evaluate. Defaults to None.
cv (int, optional) – The number of cross-validation folds. Defaults to 5.
n_trials (int, optional) – The number of trials. Defaults to 10.
to_csv (bool, optional) – Whether to save results to a CSV file. Defaults to True.
saved_file_path (str, optional) – The path to save the CSV file. Defaults to ‘history/results_cross_val_score.csv’.
**kwargs – Additional keyword arguments for cross_val_score.

Returns

The comparison results.

Return type

pandas.DataFrame

compare_cross_validate(X, y, metrics=None, cv=5, return_train_score=True, n_trials=10, to_csv=True, saved_file_path='history/results_cross_validate.csv', **kwargs)[source]¶

Performs cross-validation for model comparison.

Compares different MhaElm models using cross-validation.

Parameters

X (array-like) – The feature matrix.
y (array-like) – The target vector.
metrics (list, optional) – A list of metric names. Defaults to None.
cv (int, optional) – The number of cross-validation folds. Defaults to 5.
return_train_score (bool, optional) – Whether to return train scores. Defaults to True.
n_trials (int, optional) – The number of trials. Defaults to 10.
to_csv (bool, optional) – Whether to save results to a CSV file. Defaults to True.
saved_file_path (str, optional) – The path to save the CSV file. Defaults to ‘history/results_cross_validate.csv’.
**kwargs – Additional keyword arguments for cross_validate.

Returns

The comparison results.

Return type

pandas.DataFrame

compare_train_test(X_train, y_train, X_test, y_test, metrics=None, n_trials=10, to_csv=True, saved_file_path='history/results_train_test.csv')[source]¶

Compares models using train-test split.

Compares different MhaElm models using train-test split evaluation.

Parameters

X_train (array-like) – The training feature matrix.
y_train (array-like) – The training target vector.
X_test (array-like) – The testing feature matrix.
y_test (array-like) – The testing target vector.
metrics (list, optional) – A list of metric names. Defaults to None.
n_trials (int, optional) – The number of trials. Defaults to 10.
to_csv (bool, optional) – Whether to save results to a CSV file. Defaults to True.
saved_file_path (str, optional) – The path to save the CSV file. Defaults to ‘history/results_train_test.csv’.

Returns

The comparison results.

Return type

pandas.DataFrame

intelelm.model.automated_tuner module¶

class intelelm.model.automated_tuner.AutomatedMhaElmTuner(task='classification', param_dict=None, search_method='gridsearch', scoring=None, cv=3, **kwargs)[source]¶

Bases: object

Automated hyperparameter tuner for MhaElm models.

Performs hyperparameter tuning for MhaElm models using either GridSearchCV or RandomizedSearchCV. Provides an interface for fitting and predicting using the best found model.

model_class¶

The MhaElm model class (MhaElmRegressor or MhaElmClassifier).

Type: class

param_grid¶

The parameter grid for hyperparameter tuning.

Type: dict

search_method¶

The optimization method (‘gridsearch’ or ‘randomsearch’).

Type: str

kwargs¶

Additional keyword arguments for the search method.

Type: dict

searcher¶

The searcher

Type: GridSearchCV or RandomizedSearchCV

best_estimator_¶

The best estimator found during tuning.

Type: sklearn.base.BaseEstimator

best_params_¶

The best hyperparameters found during tuning.

Type: dict

fit(X, y)[source]¶: Fits the tuner to the data and tunes hyperparameters.

predict(X)[source]¶: Predicts using the best estimator.

fit(X, y)[source]¶

Fits the tuner to the data and tunes the hyperparameters.

Parameters

X (array-like) – Training features.
y (array-like) – Training target values.

Returns

Fitted tuner object.

Return type

self

predict(X)[source]¶

Predicts using the best estimator found during tuning.

Parameters: X (array-like) – Data to predict.
Returns: Predicted values.
Return type: array-like

intelelm.model.base_elm module¶

class intelelm.model.base_elm.BaseElm(layer_sizes=(10,), act_name='relu')[source]¶

Bases: sklearn.base.BaseEstimator

class BaseElm(BaseEstimator):

A base class for implementing Extreme Learning Machines (ELM) with support for both classification and regression tasks.

layer_sizeslist: List containing the sizes of each layer in the network.
act_namestr: The name of the activation function to be used.
networkobject: The ELM network object.
loss_trainlist: List of loss values recorded during training.
n_labelsint: Number of labels in the dataset.

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) → Optional[intelelm.model.base_elm.MultiLayerELM][source]¶

Parameters

X (ndarray) – Input data used to train the network.
y (ndarray) – Target values corresponding to the input data X.

Returns

Returns an instance of the MultiLayerELM class if creation is successful, otherwise, returns None.

Return type

Optional[“MultiLayerELM”]

evaluate(y_true, y_pred, list_metrics=None)[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) – 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)[source]¶

Parameters

X (array-like of shape (n_samples, n_features)) – Training data.
y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – Target values.

get_weights()[source]¶

Retrieves the current weights of the neural network.

Returns: A list containing the weights of the neural network.
Return type: list

static load_model(load_path='history', filename='model.pkl')[source]¶

Parameters

load_path (str, optional) – Directory path where the model file is located. Defaults to “history”.
filename (str) – Name of the file to be loaded. If the filename doesn’t end with “.pkl”, the extension is automatically added.

Returns

The model loaded from the specified pickle file.

Return type

object

predict(X, return_prob=False)[source]¶

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')[source]¶

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')[source]¶

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')[source]¶

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')[source]¶

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)[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 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)[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 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.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.model.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.model.base_elm.BaseMhaElm(layer_sizes=(10,), act_name='elu', obj_name=None, optim='BaseGA', optim_params=None, seed=None, verbose=True, lb=None, ub=None, mode='single', n_workers=None, termination=None)[source]¶

Bases: intelelm.model.base_elm.BaseElm

An optimization-based extension of the BaseElm class, designed for flexible optimization and parameter tuning of networks utilizing various optimizers from the Mealpy library. It supports different activation functions, objectives, and optimization strategies.

SUPPORTED_OPTIMIZERS¶

List of supported optimizers, obtained from the Mealpy library.

Type: list

SUPPORTED_CLS_OBJECTIVES¶

Dictionary of supported classification objectives.

Type: dict

SUPPORTED_REG_OBJECTIVES¶

Dictionary of supported regression objectives.

Type: dict

obj_name¶

Name of the objective function used for optimization.

Type: str

optim¶

Name or instance of the optimizer used for optimization.

Type: str or Optimizer

optim_params¶

Parameters for configuring the optimizer.

Type: dict

verbose¶

Whether to display optimization logs.

Type: bool

seed¶

Seed for random number generation.

Type: int

lb¶

Lower bounds for optimization variables.

Type: list, tuple, np.ndarray, int, or float

ub¶

Upper bounds for optimization variables.

Type: list, tuple, np.ndarray, int, or float

mode¶

Optimization mode, e.g., ‘single’ or ‘multi’.

Type: str

n_workers¶

Number of workers for parallel optimization.

Type: int

termination¶

Termination criteria for the optimization process.

Type: object

__init__(layer_sizes=(10,), act_name="elu", obj_name=None, optim="BaseGA", optim_params=None, seed=None, verbose=True,: lb=None, ub=None, mode=’single’, n_workers=None, termination=None)

Initializes the BaseMhaElm with specified parameters.

get_name()[source]¶: Returns the name of the optimizer used, appended with -ELM.

set_params(**params)[source]¶: Sets the parameters for the optimizer and updates parent class parameters.

set_optimizer(optim)[source]¶: Sets the optimizer attribute.

set_optim_paras(optim_params)[source]¶: Sets the optimizer parameters attribute.

set_optimizer_object(optim=None, optim_params=None)[source]¶: Sets the optimizer object with the specified parameters.

set_seed(seed)[source]¶: Sets the random seed for the class.

objective_function(solution=None)[source]¶: Placeholder fitness function to be overridden by the subclass or user.

set_lb_ub(lb=None, ub=None, n_dims=None)[source]¶: Validates and sets the lower and upper bounds for optimization.

_get_minmax(obj_name=None)¶: Retrieves the minmax value for the specified objective name.

fit(X, y)[source]¶: Fits the model to the provided data using the specified optimization parameters.

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', 'DevFOX', '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)[source]¶

Parameters

X (The features data, np.ndarray) –
y (The ground truth data) –

get_name()[source]¶

objective_function(solution=None)[source]¶

Evaluates the fitness function for classification metrics based on the provided solution.

Parameters: solution (np.ndarray, default=None) – The proposed solution to evaluate.
Returns: result – The fitness value, representing the loss for the current solution.
Return type: float

static set_lb_ub(lb=None, ub=None, n_dims=None)[source]¶

Validates and sets the lower and upper bounds for optimization.

Parameters

lb (list, tuple, np.ndarray, int, or float, optional) – The lower bounds for weights and biases in network.
ub (list, tuple, np.ndarray, int, or float, optional) – The upper bounds for weights and biases in network.
n_dims (int) – The number of dimensions.

Returns

A tuple containing validated lower and upper bounds.

Return type

tuple

Raises

ValueError – If the bounds are not valid.

set_optim_paras(optim_params)[source]¶

set_optimizer(optim)[source]¶

set_optimizer_object(optim=None, optim_params=None)[source]¶

Validates the real optimizer based on the provided optim and optim_pras.

Parameters

optim (str or Optimizer) – The optimizer name or instance to be set.
optim_params (dict, optional) – Parameters to configure the optimizer.

Returns

An instance of the selected optimizer.

Return type

Optimizer

Raises

TypeError – If the provided optimizer is neither a string nor an instance of Optimizer.

set_params(**params)[source]¶

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

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

set_seed(seed)[source]¶

Parameters: seed (int) – The seed value to initialize the random number generator.

class intelelm.model.base_elm.MultiLayerELM(layer_sizes=(10,), act_name='relu', seed=None)[source]¶

Bases: object

Initializes the Multi-Layer ELM model.

Parameters

layer_sizes (list of int) – List of integers, where each integer represents the number of neurons in the respective hidden layers.
act_name (str, optional) – Activation function to be used in the hidden layers (“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 is ‘relu’.
seed (int, optional) – Seed for random number generator. Default is None.

decode(solution_vector, X, y)[source]¶

Decode a 1-D solution vector into the weights and biases of the network.

Parameters: - solution_vector: 1-D numpy array containing the flattened weights and biases.

encode()[source]¶

Encode the current weights and biases into a 1-D vector (solution vector).

Returns: - A 1-D numpy array containing all the weights and biases of the network.

fit(X, y)[source]¶

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_ndim()[source]¶

Get the total number of dimensions (weights + biases) in the network.

Returns: - An integer representing the total number of parameters (weights + biases).

get_weights()[source]¶

predict(X)[source]¶

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)

intelelm.model.mha_elm module¶

class intelelm.model.mha_elm.MhaElmClassifier(layer_sizes=(10,), act_name='elu', obj_name='F1S', optim='BaseGA', optim_params=None, seed=None, verbose=False, lb=None, ub=None, mode='single', n_workers=None, termination=None)[source]¶

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

A classification model based on the BaseMhaElm class, designed for optimization-based training using various optimizers and classification metrics. It supports flexible network creation and evaluation using the Permetrics library.

CLS_OBJ_LOSSES¶

List of classification objective losses supported by the model.

Type: list

return_prob¶

Indicates whether the model should return probabilities for multi-class classification.

Type: bool

__init__(layer_sizes=(10,), act_name="elu", obj_name="F1S", optim="BaseGA", optim_params=None, seed=None,: verbose=False, lb=None, ub=None, mode=’single’, n_workers=None, termination=None)

Initializes the MhaElmClassifier with specified parameters.

_check_y(y)[source]¶: Validates the target labels and determines the number of unique labels.

create_network(X, y)[source]¶: Creates and initializes the classification network based on the input data and target labels.

objective_function(solution=None)[source]¶: Evaluates the fitness function for classification metrics based on the provided solution.

score(X, y, method='AS')[source]¶: Returns the selected classification metric for the given test data and labels.

scores(X, y, list_methods=('AS', 'RS'))[source]¶: Returns a dictionary of multiple classification metrics for the given test data and labels.

evaluate(y_true, y_pred, list_metrics=('AS', 'RS'))[source]¶: Evaluates the performance of predictions using a list of classification metrics.

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

create_network(X, y) → intelelm.model.base_elm.MultiLayerELM[source]¶

Parameters

X (array-like) – The input features for training the network.
y (list, tuple, or np.ndarray) – The target labels for the input data. It must be 1D and will be processed to determine the number of unique labels.

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

objective_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_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(layer_sizes=(10,), act_name='elu', obj_name='MSE', optim='BaseGA', optim_params=None, seed=None, verbose=False, obj_weights=None, lb=None, ub=None, mode='single', n_workers=None, termination=None)[source]¶

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

A regression model based on the BaseMhaElm class, designed for optimization-based training using various optimizers and regression metrics. It supports flexible network creation and evaluation using the Permetrics library.

obj_weights¶

Weights for multiple objectives in multi-output regression.

Type: list, tuple, or np.ndarray, optional

__init__(layer_sizes=(10,), act_name="elu", obj_name="MSE", optim="BaseGA", optim_params=None, seed=None,: verbose=False, obj_weights=None, lb=None, ub=None, mode=’single’, n_workers=None, termination=None)

Initializes the MhaElmRegressor with specified parameters.

create_network(X, y)[source]¶: Creates and initializes the regression network based on the input data and target values.

objective_function(solution=None)[source]¶: Evaluates the fitness function for regression metrics based on the provided solution.

score(X, y, method='RMSE')[source]¶: Returns the selected regression metric for the given test data and labels.

scores(X, y, list_methods=('MSE', 'MAE'))[source]¶: Returns a dictionary of multiple regression metrics for the given test data and labels.

evaluate(y_true, y_pred, list_metrics=('MSE', 'MAE'))[source]¶: Evaluates the performance of predictions using a list of regression metrics.

create_network(X, y) → intelelm.model.base_elm.MultiLayerELM[source]¶

Parameters

X (array-like) – The input data used to train the network.
y (array-like) – The target values. It should be a 1D vector or a 2D matrix. Accepted types: list, tuple, np.ndarray.

Returns

An instance of MultiLayerELM initialized with the specified parameters and objective scaler.

Return type

MultiLayerELM

Raises

TypeError – If y is not a list, tuple, or np.ndarray, or if y has an invalid shape that is not a 1D vector or 2D matrix, or if self.obj_weights is of an invalid type.
ValueError – If the length of self.obj_weights does not match the number of objectives.

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

objective_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_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

intelelm.model.standard_elm module¶

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

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

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

Parameters

layer_sizes (list of int, integers > 0) – A list of integers specifying the number of neurons in each hidden layer.
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(layer_sizes=(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) → intelelm.model.base_elm.MultiLayerELM[source]¶

Parameters

X (ndarray) – Input data used to train the network.
y (ndarray) – Target values corresponding to the input data X.

Returns

Returns an instance of the MultiLayerELM class if creation is successful, otherwise, returns None.

Return type

Optional[“MultiLayerELM”]

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(layer_sizes=(10,), act_name='elu', seed=None)[source]¶

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

Defines the general class of Traditional ELM model for Regression problems that inherit the BaseElm and RegressorMixin classes. It uses Moore–Penrose inverse matrix to calculate the output.

create_network(X, y) → intelelm.model.base_elm.MultiLayerELM[source]¶

Creates a MultiLayerELM network based on provided input data.

Parameters

X (array-like of shape (n_samples, n_features)) – Input samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – Target values for X.

Returns

network – An instance of MultiLayerELM configured with the specified layers and activation function.

Return type

MultiLayerELM

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