reflame package

• reflame.model package
  • reflame.model.mha_flnn module
  • reflame.model.standard_flnn module
• reflame.utils package
  • reflame.utils.activation module
  • reflame.utils.data_toolkit module
  • reflame.utils.evaluator module
  • reflame.utils.expand_util module
  • reflame.utils.validator module

Submodules

reflame.base_flnn module

class reflame.base_flnn.BaseFlnn(expand_name='chebyshev', n_funcs=4, act_name='elu')

Bases: sklearn.base.BaseEstimator

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

Parameters

• expand_name (str, default="chebyshev") – The expand function that will be used. The supported expand functions are: {“chebyshev”, “legendre”, “gegenbauer”, “laguerre”, “hermite”, “power”, “trigonometric”}

• n_funcs (int, default=4) – The first n_funcs in expand functions list will be used. Valid value from 1 to 10.

• act_name (str, default='sigmoid') – Activation function for the hidden layer. The supported activation functions are: {“relu”, “prelu”, “gelu”, “elu”, “selu”, “rrelu”, “tanh”, “hard_tanh”, “sigmoid”, “hard_sigmoid”, “swish”, “hard_swish”, “soft_plus”, “mish”, “soft_sign”, “tanh_shrink”, “soft_shrink”, “hard_shrink”}

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 BaseFlnn 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 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=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 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] = '$UNCHANGED$') → reflame.base_flnn.BaseFlnn

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] = '$UNCHANGED$') → reflame.base_flnn.BaseFlnn

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 reflame.base_flnn.BaseMhaFlnn(expand_name='chebyshev', n_funcs=4, act_name='elu', obj_name=None, optimizer='BaseGA', optimizer_paras=None, verbose=True)

Bases: reflame.base_flnn.BaseFlnn

Defines the most general class for Metaheuristic-based FLNN model that inherits the BaseFlnn class

Parameters

• expand_name (str, default="chebyshev") – The expand function that will be used. The supported expand functions are: {“chebyshev”, “legendre”, “gegenbauer”, “laguerre”, “hermite”, “power”, “trigonometric”}

• n_funcs (int, default=4) – The first n_funcs in expand functions list will be used. Valid value from 1 to 10.

• act_name (str, default='sigmoid') – Activation function for the hidden layer. The supported activation functions are: {“relu”, “prelu”, “gelu”, “elu”, “selu”, “rrelu”, “tanh”, “hard_tanh”, “sigmoid”, “hard_sigmoid”, “swish”, “hard_swish”, “soft_plus”, “mish”, “soft_sign”, “tanh_shrink”, “soft_shrink”, “hard_shrink”}

• 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.

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,), save_population=False)

objective_function(solution=None)

set_fit_request(*, lb: Union[bool, None, str] = '$UNCHANGED$', save_population: Union[bool, None, str] = '$UNCHANGED$', ub: Union[bool, None, str] = '$UNCHANGED$') → reflame.base_flnn.BaseMhaFlnn

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.

• save_population (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for save_population 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] = '$UNCHANGED$') → reflame.base_flnn.BaseMhaFlnn

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] = '$UNCHANGED$') → reflame.base_flnn.BaseMhaFlnn

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 reflame.base_flnn.FLNN(size_input=5, size_output=1, expand_name='chebyshev', n_funcs=4, act_name='elu')

Bases: object

This class defines the general Functional Link Neural Network (FLNN) model

Parameters

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

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

• expand_name (str, default="chebyshev") – The expand function that will be used. The supported expand functions are: {“chebyshev”, “legendre”, “gegenbauer”, “laguerre”, “hermite”, “power”, “trigonometric”}

• n_funcs (int, default=4) – The first n_funcs in expand functions list will be used. Valid value from 1 to 10.

• act_name (str, default='sigmoid') – Activation function for the hidden layer. The supported activation functions are: {“relu”, “prelu”, “gelu”, “elu”, “selu”, “rrelu”, “tanh”, “hard_tanh”, “sigmoid”, “hard_sigmoid”, “swish”, “hard_swish”, “soft_plus”, “mish”, “soft_sign”, “tanh_shrink”, “soft_shrink”, “hard_shrink”}

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 FLNN 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)

transform_X(X)

update_weights_from_solution(solution)

reflame.base_flnn_torch module

class reflame.base_flnn_torch.BaseFlnn(expand_name='chebyshev', n_funcs=4, act_name='elu', obj_name=None, max_epochs=1000, batch_size=32, optimizer='SGD', optimizer_paras=None, verbose=False)

Bases: sklearn.base.BaseEstimator

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

Parameters

• expand_name (str, default="chebyshev") – The expand function that will be used. The supported expand functions are: {“chebyshev”, “legendre”, “gegenbauer”, “laguerre”, “hermite”, “power”, “trigonometric”}

• n_funcs (int, default=4) – The first n_funcs in expand functions list will be used. Valid value from 1 to 10.

• 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 (str, default=None) – The name of objective for the problem, also depend on the problem is classification and regression.

• max_epochs (int, default=1000) – Maximum number of epochs / iterations / generations

• batch_size (int, default=32) – The batch size

• optimizer (str, default = "SGD") – The gradient-based optimizer from Pytorch. List of supported optimizer is: [“Adadelta”, “Adagrad”, “Adam”, “Adamax”, “AdamW”, “ASGD”, “LBFGS”, “NAdam”, “RAdam”, “RMSprop”, “Rprop”, “SGD”]

• optimizer_paras (dict or None, default=None) – The dictionary parameters of the selected optimizer.

• verbose (bool, default=True) – Whether to print progress messages to stdout.

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_LOSSES = {'MAE': <class 'torch.nn.modules.loss.L1Loss'>, 'MSE': <class 'torch.nn.modules.loss.MSELoss'>}

SUPPORTED_OPTIMIZERS = ['Adadelta', 'Adagrad', 'Adam', 'Adamax', 'AdamW', 'ASGD', 'LBFGS', 'NAdam', 'RAdam', 'RMSprop', 'Rprop', 'SGD']

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 BaseFlnn 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 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=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 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] = '$UNCHANGED$') → reflame.base_flnn_torch.BaseFlnn

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] = '$UNCHANGED$') → reflame.base_flnn_torch.BaseFlnn

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 reflame.base_flnn_torch.FLNN(size_input=10, size_output=1, expand_name='chebyshev', n_funcs=4, act_name='elu')

Bases: torch.nn.modules.module.Module

SUPPORTED_ACTIVATIONS = ['threshold', 'relu', 'rrelu', 'hardtanh', 'relu6', 'sigmoid', 'hardsigmoid', 'tanh', 'silu', 'mish', 'hardswish', 'elu', 'celu', 'selu', 'glu', 'gelu', 'hardshrink', 'leakyrelu', 'logsigmoid', 'softplus', 'softshrink', 'multiheadattention', 'prelu', 'softsign', 'tanhshrink', 'softmin', 'softmax', 'logsoftmax']

SUPPORTED_EXPANDS = ['chebyshev', 'legendre', 'gegenbauer', 'laguerre', 'hermite', 'power', 'trigonometric']

SUPPORTED_N_FUNCS = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

forward(x)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

transform_X(X)