Welcome to MetaPerceptron’s documentation!¶

https://readthedocs.org/projects/metaperceptron/badge/?version=latest

https://img.shields.io/badge/Chat-on%20Telegram-blue

MetaPerceptron (Metaheuristic-optimized Multi-Layer Perceptron) is a Python library that implements variants and the traditional version of Multi-Layer Perceptron models. These include Metaheuristic-optimized MLP models (GA, PSO, WOA, TLO, DE, …) and Gradient Descent-optimized MLP models (SGD, Adam, Adelta, Adagrad, …). It provides a comprehensive list of optimizers for training MLP models and is also compatible with the Scikit-Learn library. With MetaPerceptron, you can perform searches and hyperparameter tuning using the features provided by the Scikit-Learn library.

Free software: GNU General Public License (GPL) V3 license
Provided Estimator: MlpRegressor, MlpClassifier, MhaMlpRegressor, MhaMlpClassifier
Total Metaheuristic-based MLP Regressor: > 200 Models
Total Metaheuristic-based MLP Classifier: > 200 Models
Total Gradient Descent-based MLP Regressor: 12 Models
Total Gradient Descent-based MLP Classifier: 12 Models
Supported performance metrics: >= 67 (47 regressions and 20 classifications)
Supported objective functions (as fitness functions or loss functions): >= 67 (47 regressions and 20 classifications)
Documentation: https://metaperceptron.readthedocs.io
Python versions: >= 3.8.x
Dependencies: numpy, scipy, scikit-learn, pandas, mealpy, permetrics, torch, skorch

Installation¶

Install the current PyPI release:
```
$ pip install metaperceptron==1.1.0
```

Install directly from source code:

$ git clone https://github.com/thieu1995/metaperceptron.git
$ cd metaperceptron
$ python setup.py install

In case, you want to install the development version from Github:
```
$ pip install git+https://github.com/thieu1995/metaperceptron
```

After installation, you can import MetaPerceptron as any other Python module:

$ python
>>> import metaperceptron
>>> metaperceptron.__version__

Examples¶

In this section, we will explore the usage of the MetaPerceptron model with the assistance of a dataset. While all the preprocessing steps mentioned below can be replicated using Scikit-Learn, we have implemented some utility functions to provide users with convenience and faster usage.

Combine MetaPerceptron library like a normal library with scikit-learn:

### Step 1: Importing the libraries
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler, LabelEncoder
from metaperceptron import MlpRegressor, MlpClassifier, MhaMlpRegressor, MhaMlpClassifier

#### Step 2: Reading the dataset
dataset = pd.read_csv('Position_Salaries.csv')
X = dataset.iloc[:, 1:2].values
y = dataset.iloc[:, 2].values

#### Step 3: Next, split dataset into train and test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=True, random_state=100)

#### Step 4: Feature Scaling
scaler_X = MinMaxScaler()
scaler_X.fit(X_train)
X_train = scaler_X.transform(X_train)
X_test = scaler_X.transform(X_test)

le_y = LabelEncoder()  # This is for classification problem only
le_y.fit(y)
y_train = le_y.transform(y_train)
y_test = le_y.transform(y_test)

#### Step 5: Fitting MLP-based model to the dataset

##### 5.1: Use standard MLP model for regression problem
regressor = MlpRegressor(hidden_size=50, act1_name="tanh", act2_name="sigmoid", obj_name="MSE",
         max_epochs=1000, batch_size=32, optimizer="SGD", optimizer_paras=None, verbose=False)
regressor.fit(X_train, y_train)

##### 5.2: Use standard MLP model for classification problem
classifer = MlpClassifier(hidden_size=50, act1_name="tanh", act2_name="sigmoid", obj_name="NLLL",
         max_epochs=1000, batch_size=32, optimizer="SGD", optimizer_paras=None, verbose=False)
classifer.fit(X_train, y_train)

##### 5.3: Use Metaheuristic-based MLP model for regression problem
print(MhaMlpClassifier.SUPPORTED_OPTIMIZERS)
print(MhaMlpClassifier.SUPPORTED_REG_OBJECTIVES)
opt_paras = {"name": "GA", "epoch": 10, "pop_size": 30}
model = MhaMlpRegressor(hidden_size=50, act1_name="tanh", act2_name="sigmoid",
         obj_name="MSE", optimizer="OriginalWOA", optimizer_paras=opt_paras, verbose=True)
regressor.fit(X_train, y_train)

##### 5.4: Use Metaheuristic-based MLP model for classification problem
print(MhaMlpClassifier.SUPPORTED_OPTIMIZERS)
print(MhaMlpClassifier.SUPPORTED_CLS_OBJECTIVES)
opt_paras = {"name": "GA", "epoch": 10, "pop_size": 30}
classifier = MhaMlpClassifier(hidden_size=50, act1_name="tanh", act2_name="softmax",
         obj_name="CEL", optimizer="OriginalWOA", optimizer_paras=opt_paras, verbose=True)
classifier.fit(X_train, y_train)

#### Step 6: Predicting a new result
y_pred = regressor.predict(X_test)

y_pred_cls = classifier.predict(X_test)
y_pred_label = le_y.inverse_transform(y_pred_cls)

#### Step 7: Calculate metrics using score or scores functions.
print("Try my AS metric with score function")
print(regressor.score(X_test, y_test, method="AS"))

print("Try my multiple metrics with scores function")
print(classifier.scores(X_test, y_test, list_methods=["AS", "PS", "F1S", "CEL", "BSL"]))

Utilities everything that metaperceptron provided:

### Step 1: Importing the libraries
from metaperceptron import Data, MlpRegressor, MlpClassifier, MhaMlpRegressor, MhaMlpClassifier
from sklearn.datasets import load_digits

#### Step 2: Reading the dataset
X, y = load_digits(return_X_y=True)
data = Data(X, y)

#### Step 3: Next, split dataset into train and test set
data.split_train_test(test_size=0.2, shuffle=True, random_state=100)

#### Step 4: Feature Scaling
data.X_train, scaler_X = data.scale(data.X_train, scaling_methods=("minmax"))
data.X_test = scaler_X.transform(data.X_test)

data.y_train, scaler_y = data.encode_label(data.y_train)  # This is for classification problem only
data.y_test = scaler_y.transform(data.y_test)

#### Step 5: Fitting MLP-based model to the dataset
##### 5.1: Use standard MLP model for regression problem
regressor = MlpRegressor(hidden_size=50, act1_name="tanh", act2_name="sigmoid", obj_name="MSE",
         max_epochs=1000, batch_size=32, optimizer="SGD", optimizer_paras=None, verbose=False)
regressor.fit(data.X_train, data.y_train)

##### 5.2: Use standard MLP model for classification problem
classifer = MlpClassifier(hidden_size=50, act1_name="tanh", act2_name="sigmoid", obj_name="NLLL",
         max_epochs=1000, batch_size=32, optimizer="SGD", optimizer_paras=None, verbose=False)
classifer.fit(data.X_train, data.y_train)

##### 5.3: Use Metaheuristic-based MLP model for regression problem
print(MhaMlpClassifier.SUPPORTED_OPTIMIZERS)
print(MhaMlpClassifier.SUPPORTED_REG_OBJECTIVES)
opt_paras = {"name": "GA", "epoch": 10, "pop_size": 30}
model = MhaMlpRegressor(hidden_size=50, act1_name="tanh", act2_name="sigmoid",
         obj_name="MSE", optimizer="OriginalWOA", optimizer_paras=opt_paras, verbose=True)
regressor.fit(data.X_train, data.y_train)

##### 5.4: Use Metaheuristic-based MLP model for classification problem
print(MhaMlpClassifier.SUPPORTED_OPTIMIZERS)
print(MhaMlpClassifier.SUPPORTED_CLS_OBJECTIVES)
opt_paras = {"name": "GA", "epoch": 10, "pop_size": 30}
classifier = MhaMlpClassifier(hidden_size=50, act1_name="tanh", act2_name="softmax",
         obj_name="CEL", optimizer="OriginalWOA", optimizer_paras=opt_paras, verbose=True)
classifier.fit(data.X_train, data.y_train)

#### Step 6: Predicting a new result
y_pred = regressor.predict(data.X_test)

y_pred_cls = classifier.predict(data.X_test)
y_pred_label = scaler_y.inverse_transform(y_pred_cls)

#### Step 7: Calculate metrics using score or scores functions.
print("Try my AS metric with score function")
print(regressor.score(data.X_test, data.y_test, method="AS"))

print("Try my multiple metrics with scores function")
print(classifier.scores(data.X_test, data.y_test, list_methods=["AS", "PS", "F1S", "CEL", "BSL"]))

A real-world dataset contains features that vary in magnitudes, units, and range. We would suggest performing normalization when the scale of a feature is irrelevant or misleading. Feature Scaling basically helps to normalize the data within a particular range.

metaperceptron package¶

metaperceptron.core package¶

metaperceptron.core.base_mlp_numpy module¶

class metaperceptron.core.base_mlp_numpy.BaseMhaMlp(hidden_size=50, act1_name='tanh', act2_name='sigmoid', obj_name=None, optimizer='OriginalWOA', optimizer_paras=None, verbose=True)[source]¶

Bases: BaseEstimator

Defines the most general class for Metaheuristic-based MLP model that inherits the BaseMlpNumpy class

Parameters:

hidden_size (int, default=50) – The number of hidden nodes
act1_name (str, default='tanh') – Activation function for the hidden layer. The supported activation functions are: [“none”, “relu”, “leaky_relu”, “celu”, “prelu”, “gelu”, “elu”, “selu”, “rrelu”, “tanh”, “hard_tanh”, “sigmoid”, “hard_sigmoid”, “log_sigmoid”, “swish”, “hard_swish”, “soft_plus”, “mish”, “soft_sign”, “tanh_shrink”, “soft_shrink”, “hard_shrink”, “softmin”, “softmax”, “log_softmax”, “silu”]
act2_name (str, default='sigmoid') – Activation function for the hidden layer. The supported activation functions are: [“none”, “relu”, “leaky_relu”, “celu”, “prelu”, “gelu”, “elu”, “selu”, “rrelu”, “tanh”, “hard_tanh”, “sigmoid”, “hard_sigmoid”, “log_sigmoid”, “swish”, “hard_swish”, “soft_plus”, “mish”, “soft_sign”, “tanh_shrink”, “soft_shrink”, “hard_shrink”, “softmin”, “softmax”, “log_softmax”, “silu”]
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 = "OriginalWOA") – 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.

CLS_OBJ_LOSSES = None¶

SUPPORTED_ACTIVATIONS = ['none', 'relu', 'leaky_relu', 'celu', 'prelu', 'gelu', 'elu', 'selu', 'rrelu', 'tanh', 'hard_tanh', 'sigmoid', 'hard_sigmoid', 'log_sigmoid', 'swish', 'hard_swish', 'soft_plus', 'mish', 'soft_sign', 'tanh_shrink', 'soft_shrink', 'hard_shrink', 'softmin', 'softmax', 'log_softmax', 'silu']¶

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_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_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'}¶

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'}¶

create_network(X, y)[source]¶

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, lb=(-1.0,), ub=(1.0,), save_population=False, obj_weights=None)[source]¶

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

objective_function(solution=None)[source]¶

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

Inherit the predict function from BaseMlpNumpy 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_evaluation_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_training_loss(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_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 all 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 all metrics from Permetrics library: https://github.com/thieu1995/permetrics

Returns:

results – The results of the list metrics

Return type:

dict

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.
obj_weights (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for obj_weights 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: bool | None | str = '$UNCHANGED$') → BaseMhaMlp¶

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: bool | None | str = '$UNCHANGED$') → BaseMhaMlp¶

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 metaperceptron.core.base_mlp_numpy.MlpNumpy(input_size=5, hidden_size=10, output_size=1, act1_name='tanh', act2_name='sigmoid')[source]¶

Bases: object

This class defines the general Multi-Layer Perceptron (MLP) model using Numpy

Parameters:

input_size (int, default=5) – The number of input nodes
hidden_size (int, default=10) – The number of hidden nodes
output_size (int, default=1) – The number of output nodes
act1_name (str, default='tanh') – Activation function for the hidden layer. The supported activation functions are: [“none”, “relu”, “leaky_relu”, “celu”, “prelu”, “gelu”, “elu”, “selu”, “rrelu”, “tanh”, “hard_tanh”, “sigmoid”, “hard_sigmoid”, “log_sigmoid”, “swish”, “hard_swish”, “soft_plus”, “mish”, “soft_sign”, “tanh_shrink”, “soft_shrink”, “hard_shrink”, “softmin”, “softmax”, “log_softmax”, “silu”]
act2_name (str, default='sigmoid') – Activation function for the hidden layer. The supported activation functions are: [“none”, “relu”, “leaky_relu”, “celu”, “prelu”, “gelu”, “elu”, “selu”, “rrelu”, “tanh”, “hard_tanh”, “sigmoid”, “hard_sigmoid”, “log_sigmoid”, “swish”, “hard_swish”, “soft_plus”, “mish”, “soft_sign”, “tanh_shrink”, “soft_shrink”, “hard_shrink”, “softmin”, “softmax”, “log_softmax”, “silu”]

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 MLP model.

Return type:

object

forward(inputs)[source]¶

get_weights()[source]¶

get_weights_size()[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)

set_weights(weights)[source]¶

update_weights_from_solution(solution)[source]¶

metaperceptron.core.base_mlp_torch module¶

class metaperceptron.core.base_mlp_torch.BaseMlpTorch(hidden_size=50, act1_name='tanh', act2_name='sigmoid', obj_name=None, max_epochs=1000, batch_size=32, optimizer='SGD', optimizer_paras=None, verbose=False)[source]¶

Bases: BaseEstimator

Defines the most general class for traditional MLP models that inherits the BaseEstimator class of Scikit-Learn library.

Parameters:

hidden_size (int, default=50) – The hidden size of MLP network (This network only has single hidden layer).
act1_name (str, defeault="tanh") – This is activation for hidden layer. The supported activation are: {“none”, “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”}.
act2_name (str, defeault="sigmoid") – This is activation for output layer. The supported activation are: {“none”, “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”}.
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)[source]¶

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]¶

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

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

Inherit the predict function from BaseMlp 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_evaluation_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_training_loss(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_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 all 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 all 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: bool | None | str = '$UNCHANGED$') → BaseMlpTorch¶

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: bool | None | str = '$UNCHANGED$') → BaseMlpTorch¶

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 metaperceptron.core.base_mlp_torch.MlpTorch(input_size, hidden_size, output_size, act1_name='tanh', act2_name='sigmoid')[source]¶

Bases: Module

Define the MLP model

SUPPORTED_ACTIVATIONS = ['none', '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']¶

forward(x)[source]¶

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¶

metaperceptron.core.mha_mlp module¶

class metaperceptron.core.mha_mlp.MhaMlpClassifier(hidden_size=50, act1_name='tanh', act2_name='sigmoid', obj_name='CEL', optimizer='OriginalWOA', optimizer_paras=None, verbose=True)[source]¶

Bases: BaseMhaMlp, ClassifierMixin

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

Parameters:

hidden_size (int, default=50) – The number of hidden nodes
act1_name (str, default='tanh') – Activation function for the hidden layer. The supported activation functions are: [“none”, “relu”, “leaky_relu”, “celu”, “prelu”, “gelu”, “elu”, “selu”, “rrelu”, “tanh”, “hard_tanh”, “sigmoid”, “hard_sigmoid”, “log_sigmoid”, “swish”, “hard_swish”, “soft_plus”, “mish”, “soft_sign”, “tanh_shrink”, “soft_shrink”, “hard_shrink”, “softmin”, “softmax”, “log_softmax”, “silu”]
act2_name (str, default='sigmoid') – Activation function for the hidden layer. The supported activation functions are: [“none”, “relu”, “leaky_relu”, “celu”, “prelu”, “gelu”, “elu”, “selu”, “rrelu”, “tanh”, “hard_tanh”, “sigmoid”, “hard_sigmoid”, “log_sigmoid”, “swish”, “hard_swish”, “soft_plus”, “mish”, “soft_sign”, “tanh_shrink”, “soft_shrink”, “hard_shrink”, “softmin”, “softmax”, “log_softmax”, “silu”]
obj_name (str, default="MSE") – Current supported objective functions, please check it here: https://github.com/thieu1995/permetrics
optimizer (str or instance of Optimizer class (from Mealpy library), default = "OriginalWOA") – 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.

Examples

>>> from metaperceptron import Data, MhaMlpClassifier
>>> 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)
>>> data.X_train_scaled, scaler = data.scale(data.X_train, method="MinMaxScaler")
>>> data.X_test_scaled = scaler.transform(data.X_test)
>>> opt_paras = {"name": "GA", "epoch": 10, "pop_size": 30}
>>> print(MhaMlpClassifier.SUPPORTED_CLS_OBJECTIVES)
{'PS': 'max', 'NPV': 'max', 'RS': 'max', ...., 'KLDL': 'min', 'BSL': 'min'}
>>> model = MhaMlpClassifier(hidden_size=50, act1_name="tanh", act2_name="sigmoid",
>>>             obj_name="NPV", optimizer="OriginalWOA", optimizer_paras=opt_paras, verbose=True)
>>> model.fit(data.X_train_scaled, data.y_train)
>>> pred = model.predict(data.X_test_scaled)
>>> print(pred)
array([1, 0, 1, 0, 1])

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

create_network(X, y) → Tuple[MlpNumpy, ObjectiveScaler][source]¶

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

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

Parameters:

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

Returns:

results – The results of the list metrics

Return type:

dict

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

Returns:

results – The results of the list metrics

Return type:

dict

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.
obj_weights (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for obj_weights 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: bool | None | str = '$UNCHANGED$') → MhaMlpClassifier¶

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: bool | None | str = '$UNCHANGED$') → MhaMlpClassifier¶

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 metaperceptron.core.mha_mlp.MhaMlpRegressor(hidden_size=50, act1_name='tanh', act2_name='sigmoid', obj_name='MSE', optimizer='OriginalWOA', optimizer_paras=None, verbose=True)[source]¶

Bases: BaseMhaMlp, RegressorMixin

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

Parameters:

hidden_size (int, default=50) – The number of hidden nodes
act1_name (str, default='tanh') – Activation function for the hidden layer. The supported activation functions are: [“none”, “relu”, “leaky_relu”, “celu”, “prelu”, “gelu”, “elu”, “selu”, “rrelu”, “tanh”, “hard_tanh”, “sigmoid”, “hard_sigmoid”, “log_sigmoid”, “swish”, “hard_swish”, “soft_plus”, “mish”, “soft_sign”, “tanh_shrink”, “soft_shrink”, “hard_shrink”, “softmin”, “softmax”, “log_softmax”, “silu”]
act2_name (str, default='sigmoid') – Activation function for the hidden layer. The supported activation functions are: [“none”, “relu”, “leaky_relu”, “celu”, “prelu”, “gelu”, “elu”, “selu”, “rrelu”, “tanh”, “hard_tanh”, “sigmoid”, “hard_sigmoid”, “log_sigmoid”, “swish”, “hard_swish”, “soft_plus”, “mish”, “soft_sign”, “tanh_shrink”, “soft_shrink”, “hard_shrink”, “softmin”, “softmax”, “log_softmax”, “silu”]
obj_name (str, default="MSE") – Current supported objective functions, please check it here: https://github.com/thieu1995/permetrics
optimizer (str or instance of Optimizer class (from Mealpy library), default = "OriginalWOA") – 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.

Examples

>>> from metaperceptron import MhaMlpRegressor, Data
>>> from sklearn.datasets import make_regression
>>> X, y = make_regression(n_samples=200, random_state=1)
>>> data = Data(X, y)
>>> data.split_train_test(test_size=0.2, random_state=1)
>>> data.X_train_scaled, scaler = data.scale(data.X_train, method="MinMaxScaler")
>>> data.X_test_scaled = scaler.transform(data.X_test)
>>> opt_paras = {"name": "GA", "epoch": 10, "pop_size": 30}
>>> model = MhaMlpRegressor(hidden_size=15, act1_name="relu", act2_name="sigmoid",
>>>             obj_name="MSE", optimizer="BaseGA", optimizer_paras=opt_paras, verbose=True)
>>> model.fit(data.X_train_scaled, data.y_train)
>>> pred = model.predict(data.X_test_scaled)
>>> print(pred)

create_network(X, y) → Tuple[MlpNumpy, ObjectiveScaler][source]¶

Returns:

network (MLP, an instance of MLP network)
obj_scaler (ObjectiveScaler, the objective scaler that used to scale output)

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

Returns:

results – The results of the list metrics

Return type:

dict

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.
obj_weights (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for obj_weights 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: bool | None | str = '$UNCHANGED$') → MhaMlpRegressor¶

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: bool | None | str = '$UNCHANGED$') → MhaMlpRegressor¶

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

metaperceptron.core.traditional_mlp module¶

class metaperceptron.core.traditional_mlp.MlpClassifier(hidden_size=50, act1_name='tanh', act2_name='sigmoid', obj_name='NLLL', max_epochs=1000, batch_size=32, optimizer='SGD', optimizer_paras=None, verbose=False, **kwargs)[source]¶

Bases: BaseMlpTorch

Defines the class for traditional MLP network for Classification problems that inherit the BaseMlpTorch class

Parameters:

hidden_size (int, default=50) – The hidden size of MLP network (This network only has single hidden layer).
act1_name (str, defeault="tanh") – This is activation for hidden layer. The supported activation are: {“none”, “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”}.
act2_name (str, defeault="sigmoid") – This is activation for output layer. The supported activation are: {“none”, “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”}.
obj_name (str, default="NLLL") – The name of objective for classification problem (binary and multi-class classification)
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.

Examples

>>> from metaperceptron import MlpClassifier, Data
>>> from sklearn.datasets import make_regression
>>>
>>> ## Make dataset
>>> X, y = make_regression(n_samples=200, n_features=10, random_state=1)
>>> ## Load data object
>>> data = Data(X, y)
>>> ## Split train and test
>>> data.split_train_test(test_size=0.2, random_state=1, inplace=True)
>>> ## Scale dataset
>>> data.X_train, scaler = data.scale(data.X_train, scaling_methods=("minmax"))
>>> data.X_test = scaler.transform(data.X_test)
>>> ## Create model
>>> model = MlpClassifier(hidden_size=25, act1_name="tanh", act2_name="sigmoid", obj_name="BCEL",
>>>                 max_epochs=10, batch_size=32, optimizer="SGD", optimizer_paras=None, verbose=True)
>>> ## Train the model
>>> model.fit(data.X_train, data.y_train)
>>> ## Test the model
>>> y_pred = model.predict(data.X_test)
>>> ## Calculate some metrics
>>> print(model.score(X=data.X_test, y=data.y_test, method="RMSE"))
>>> print(model.scores(X=data.X_test, y=data.y_test, list_methods=["R2", "NSE", "MAPE"]))
>>> print(model.evaluate(y_true=data.y_test, y_pred=y_pred, list_metrics=["R2", "NSE", "MAPE", "NNSE"]))

CLS_OBJ_BINARY_1 = ['PNLLL', 'HEL', 'BCEL', 'CEL', 'BCELL']¶

CLS_OBJ_BINARY_2 = ['NLLL']¶

CLS_OBJ_LOSSES = ['CEL', 'HEL', 'KLDL']¶

CLS_OBJ_MULTI = ['NLLL', 'CEL']¶

SUPPORTED_LOSSES = {'BCEL': <class 'torch.nn.modules.loss.BCELoss'>, 'BCELL': <class 'torch.nn.modules.loss.BCEWithLogitsLoss'>, 'CEL': <class 'torch.nn.modules.loss.CrossEntropyLoss'>, 'GNLLL': <class 'torch.nn.modules.loss.GaussianNLLLoss'>, 'HEL': <class 'torch.nn.modules.loss.HingeEmbeddingLoss'>, 'KLDL': <class 'torch.nn.modules.loss.KLDivLoss'>, 'NLLL': <class 'torch.nn.modules.loss.NLLLoss'>, 'PNLLL': <class 'torch.nn.modules.loss.PoissonNLLLoss'>}¶

create_network(X, y) → Tuple[NeuralNetClassifier, ObjectiveScaler][source]¶

Returns:

network (MLP, an instance of MLP network)
obj_scaler (ObjectiveScaler, the objective scaler that used to scale output)

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

fit(X, y)[source]¶

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 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 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: bool | None | str = '$UNCHANGED$') → MlpClassifier¶

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: bool | None | str = '$UNCHANGED$') → MlpClassifier¶

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 metaperceptron.core.traditional_mlp.MlpRegressor(hidden_size=50, act1_name='tanh', act2_name='sigmoid', obj_name='MSE', max_epochs=1000, batch_size=32, optimizer='SGD', optimizer_paras=None, verbose=False, **kwargs)[source]¶

Bases: BaseMlpTorch

Defines the class for traditional MLP network for Regression problems that inherit the BaseMlpTorch and RegressorMixin classes.

Parameters:

hidden_size (int, default=50) – The hidden size of MLP network (This network only has single hidden layer).
act1_name (str, defeault="tanh") – This is activation for hidden layer. The supported activation are: {“none”, “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”}.
act2_name (str, defeault="sigmoid") – This is activation for output layer. The supported activation are: {“none”, “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”}.
obj_name (str, default="MSE") – The name of loss function for the network.
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.

Examples

>>> from metaperceptron import MlpRegressor, Data
>>> from sklearn.datasets import make_regression
>>>
>>> ## Make dataset
>>> X, y = make_regression(n_samples=200, n_features=10, random_state=1)
>>> ## Load data object
>>> data = Data(X, y)
>>> ## Split train and test
>>> data.split_train_test(test_size=0.2, random_state=1, inplace=True)
>>> ## Scale dataset
>>> data.X_train, scaler = data.scale(data.X_train, scaling_methods=("minmax"))
>>> data.X_test = scaler.transform(data.X_test)
>>> ## Create model
>>> model = MlpRegressor(hidden_size=25, act1_name="tanh", act2_name="sigmoid", obj_name="MSE",
>>>             max_epochs=10, batch_size=32, optimizer="SGD", optimizer_paras=None, verbose=True)
>>> ## Train the model
>>> model.fit(data.X_train, data.y_train)
>>> ## Test the model
>>> y_pred = model.predict(data.X_test)
>>> ## Calculate some metrics
>>> print(model.score(X=data.X_test, y=data.y_test, method="RMSE"))
>>> print(model.scores(X=data.X_test, y=data.y_test, list_methods=["R2", "NSE", "MAPE"]))
>>> print(model.evaluate(y_true=data.y_test, y_pred=y_pred, list_metrics=["R2", "NSE", "MAPE", "NNSE"]))

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

create_network(X, y)[source]¶

Returns:

network (MLP, an instance of MLP network)
obj_scaler (ObjectiveScaler, the objective scaler that used to scale output)

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

fit(X, y)[source]¶

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 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 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: bool | None | str = '$UNCHANGED$') → MlpRegressor¶

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: bool | None | str = '$UNCHANGED$') → MlpRegressor¶

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

metaperceptron.helpers package¶

metaperceptron.helpers.act_util module¶

metaperceptron.helpers.act_util.celu(x, alpha=1.0)[source]¶

metaperceptron.helpers.act_util.elu(x, alpha=1)[source]¶

metaperceptron.helpers.act_util.gelu(x, alpha=0.044715)[source]¶

metaperceptron.helpers.act_util.hard_shrink(x, alpha=0.5)[source]¶

metaperceptron.helpers.act_util.hard_sigmoid(x, lower=-2.5, upper=2.5)[source]¶

metaperceptron.helpers.act_util.hard_swish(x, lower=-3.0, upper=3.0)[source]¶

metaperceptron.helpers.act_util.hard_tanh(x, lower=-1.0, upper=1.0)[source]¶

metaperceptron.helpers.act_util.leaky_relu(x, alpha=0.01)[source]¶

metaperceptron.helpers.act_util.log_sigmoid(x)[source]¶

metaperceptron.helpers.act_util.log_softmax(x)[source]¶

metaperceptron.helpers.act_util.mish(x, beta=1.0)[source]¶

metaperceptron.helpers.act_util.none(x)[source]¶

metaperceptron.helpers.act_util.prelu(x, alpha=0.5)[source]¶

metaperceptron.helpers.act_util.relu(x)[source]¶

metaperceptron.helpers.act_util.rrelu(x, lower=0.125, upper=0.3333333333333333)[source]¶

metaperceptron.helpers.act_util.selu(x, alpha=1.67326324, scale=1.05070098)[source]¶

metaperceptron.helpers.act_util.sigmoid(x)[source]¶

metaperceptron.helpers.act_util.silu(x)¶

metaperceptron.helpers.act_util.soft_plus(x, beta=1.0)[source]¶

metaperceptron.helpers.act_util.soft_shrink(x, alpha=0.5)[source]¶

metaperceptron.helpers.act_util.soft_sign(x)[source]¶

metaperceptron.helpers.act_util.softmax(x)[source]¶

metaperceptron.helpers.act_util.softmin(x)[source]¶

metaperceptron.helpers.act_util.swish(x)[source]¶

metaperceptron.helpers.act_util.tanh(x)[source]¶

metaperceptron.helpers.act_util.tanh_shrink(x)[source]¶

metaperceptron.helpers.metric_util module¶

metaperceptron.helpers.metric_util.get_all_classification_metrics()[source]¶

metaperceptron.helpers.metric_util.get_all_regression_metrics()[source]¶

metaperceptron.helpers.metric_util.get_metrics(problem, y_true, y_pred, metrics=None, testcase='test')[source]¶

metaperceptron.helpers.preprocessor module¶

class metaperceptron.helpers.preprocessor.Data(X=None, y=None, name='Unknown')[source]¶

Bases: object

The structure of our supported Data class

Parameters:

X (np.ndarray) – The features of your data
y (np.ndarray) – The labels of your data

SUPPORT = {'scaler': ['standard', 'minmax', 'max-abs', 'log1p', 'loge', 'sqrt', 'sinh-arc-sinh', 'robust', 'box-cox', 'yeo-johnson']}¶

static check_y(y)[source]¶

static encode_label(y)[source]¶

static scale(X, scaling_methods=('standard',), list_dict_paras=None)[source]¶

set_train_test(X_train=None, y_train=None, X_test=None, y_test=None)[source]¶

Function use to set your own X_train, y_train, X_test, y_test in case you don’t want to use our split function

Parameters:

X_train (np.ndarray) –
y_train (np.ndarray) –
X_test (np.ndarray) –
y_test (np.ndarray) –

split_train_test(test_size=0.2, train_size=None, random_state=41, shuffle=True, stratify=None, inplace=True)[source]¶: The wrapper of the split_train_test function in scikit-learn library.

class metaperceptron.helpers.preprocessor.FeatureEngineering[source]¶

Bases: object

create_threshold_binary_features(X, threshold)[source]¶

Perform feature engineering to add binary indicator columns for values below the threshold. Add each new column right after the corresponding original column.

Args: X (numpy.ndarray): The input 2D matrix of shape (n_samples, n_features). threshold (float): The threshold value for identifying low values.

Returns: numpy.ndarray: The updated 2D matrix with binary indicator columns.

class metaperceptron.helpers.preprocessor.LabelEncoder[source]¶

Bases: object

Encode categorical features as integer labels.

static check_y(y)[source]¶

fit(y)[source]¶

Fit label encoder to a given set of labels.

Parameters:¶

yarray-like: Labels to encode.

fit_transform(y)[source]¶

Fit label encoder and return encoded labels.

Parameters:: y (array-like of shape (n_samples,)) – Target values.
Returns:: y – Encoded labels.
Return type:: array-like of shape (n_samples,)

inverse_transform(y)[source]¶

Transform integer labels to original labels.

Parameters:¶

yarray-like: Encoded integer labels.

Returns:¶

original_labelsarray-like: Original labels.

transform(y)[source]¶

Transform labels to encoded integer labels.

Parameters:¶

yarray-like (1-D vector): Labels to encode.

Returns:¶

encoded_labelsarray-like: Encoded integer labels.

class metaperceptron.helpers.preprocessor.TimeSeriesDifferencer(interval=1)[source]¶

Bases: object

difference(X)[source]¶

inverse_difference(diff_data)[source]¶

metaperceptron.helpers.preprocessor.get_dataset(dataset_name)[source]¶

Helper function to retrieve the data

Parameters:: dataset_name (str) – Name of the dataset
Returns:: data – The instance of Data class, that hold X and y variables.
Return type:: Data

metaperceptron.helpers.scaler_util module¶

class metaperceptron.helpers.scaler_util.BoxCoxScaler(lmbda=None)[source]¶

Bases: BaseEstimator, TransformerMixin

fit(X, y=None)[source]¶

inverse_transform(X)[source]¶

transform(X)[source]¶

class metaperceptron.helpers.scaler_util.DataTransformer(scaling_methods=('standard',), list_dict_paras=None)[source]¶

Bases: BaseEstimator, TransformerMixin

SUPPORTED_SCALERS = {'box-cox': <class 'metaperceptron.helpers.scaler_util.BoxCoxScaler'>, 'log1p': <class 'metaperceptron.helpers.scaler_util.Log1pScaler'>, 'loge': <class 'metaperceptron.helpers.scaler_util.LogeScaler'>, 'max-abs': <class 'sklearn.preprocessing._data.MaxAbsScaler'>, 'minmax': <class 'sklearn.preprocessing._data.MinMaxScaler'>, 'robust': <class 'sklearn.preprocessing._data.RobustScaler'>, 'sinh-arc-sinh': <class 'metaperceptron.helpers.scaler_util.SinhArcSinhScaler'>, 'sqrt': <class 'metaperceptron.helpers.scaler_util.SqrtScaler'>, 'standard': <class 'sklearn.preprocessing._data.StandardScaler'>, 'yeo-johnson': <class 'metaperceptron.helpers.scaler_util.YeoJohnsonScaler'>}¶

fit(X, y=None)[source]¶

inverse_transform(X)[source]¶

transform(X)[source]¶

class metaperceptron.helpers.scaler_util.Log1pScaler[source]¶

Bases: BaseEstimator, TransformerMixin

fit(X, y=None)[source]¶

inverse_transform(X)[source]¶

transform(X)[source]¶

class metaperceptron.helpers.scaler_util.LogeScaler[source]¶

Bases: BaseEstimator, TransformerMixin

fit(X, y=None)[source]¶

inverse_transform(X)[source]¶

transform(X)[source]¶

class metaperceptron.helpers.scaler_util.ObjectiveScaler(obj_name='sigmoid', ohe_scaler=None)[source]¶

Bases: object

For label scaler in classification (binary and multiple classification)

inverse_transform(data)[source]¶

transform(data)[source]¶

class metaperceptron.helpers.scaler_util.SinhArcSinhScaler(epsilon=0.1, delta=1.0)[source]¶

Bases: BaseEstimator, TransformerMixin

fit(X, y=None)[source]¶

inverse_transform(X)[source]¶

transform(X)[source]¶

class metaperceptron.helpers.scaler_util.SqrtScaler[source]¶

Bases: BaseEstimator, TransformerMixin

fit(X, y=None)[source]¶

inverse_transform(X)[source]¶

transform(X)[source]¶

class metaperceptron.helpers.scaler_util.YeoJohnsonScaler(lmbda=None)[source]¶

Bases: BaseEstimator, TransformerMixin

fit(X, y=None)[source]¶

inverse_transform(X)[source]¶

transform(X)[source]¶

metaperceptron.helpers.validator module¶

metaperceptron.helpers.validator.check_bool(name: str, value: bool, bound=(True, False))[source]¶

metaperceptron.helpers.validator.check_float(name: str, value: int, bound=None)[source]¶

metaperceptron.helpers.validator.check_int(name: str, value: int, bound=None)[source]¶

metaperceptron.helpers.validator.check_str(name: str, value: str, bound=None)[source]¶

metaperceptron.helpers.validator.check_tuple_float(name: str, values: tuple, bounds=None)[source]¶

metaperceptron.helpers.validator.check_tuple_int(name: str, values: tuple, bounds=None)[source]¶

metaperceptron.helpers.validator.is_in_bound(value, bound)[source]¶

metaperceptron.helpers.validator.is_str_in_list(value: str, my_list: list)[source]¶

Citation Request¶

Note:

If you want to understand how Metaheuristic is applied to Multi-Layer Perceptron, you need to read the paper
titled `Let a biogeography-based optimizer train your Multi-Layer Perceptron`.
The paper can be accessed at the following `this link <https://doi.org/10.1016/j.ins.2014.01.038>`_

Please include these citations if you plan to use this library:

@software{nguyen_van_thieu_2023_10251022,
  author       = {Nguyen Van Thieu},
  title        = {MetaPerceptron: Unleashing the Power of Metaheuristic-optimized Multi-Layer Perceptron - A Python Library},
  month        = dec,
  year         = 2023,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.10251021},
  url          = {https://github.com/thieu1995/MetaPerceptron}
}

@article{van2023mealpy,
  title={MEALPY: An open-source library for latest meta-heuristic algorithms in Python},
  author={Van Thieu, Nguyen and Mirjalili, Seyedali},
  journal={Journal of Systems Architecture},
  year={2023},
  publisher={Elsevier},
  doi={10.1016/j.sysarc.2023.102871}
}

@article{van2023groundwater,
  title={Groundwater level modeling using Augmented Artificial Ecosystem Optimization},
  author={Van Thieu, Nguyen and Barma, Surajit Deb and Van Lam, To and Kisi, Ozgur and Mahesha, Amai},
  journal={Journal of Hydrology},
  volume={617},
  pages={129034},
  year={2023},
  publisher={Elsevier}
}

@article{thieu2019efficient,
  title={Efficient time-series forecasting using neural network and opposition-based coral reefs optimization},
  author={Thieu Nguyen, Tu Nguyen and Nguyen, Binh Minh and Nguyen, Giang},
  journal={International Journal of Computational Intelligence Systems},
  volume={12},
  number={2},
  pages={1144--1161},
  year={2019}
}

```

If you have an open-ended or a research question, you can contact me via nguyenthieu2102@gmail.com

Important links¶

Official source code repo: https://github.com/thieu1995/metaperceptron
Official document: https://metaperceptron.readthedocs.io/
Download releases: https://pypi.org/project/metaperceptron/
Issue tracker: https://github.com/thieu1995/metaperceptron/issues
Notable changes log: https://github.com/thieu1995/metaperceptron/blob/master/ChangeLog.md
This project also related to our another projects which are “optimization” and “machine learning”, check it here:

License¶

The project is licensed under GNU General Public License (GPL) V3 license.

Welcome to MetaPerceptron’s documentation!¶

Installation¶

Examples¶

metaperceptron package¶

metaperceptron.core package¶

metaperceptron.core.base_mlp_numpy module¶

metaperceptron.core.base_mlp_torch module¶

metaperceptron.core.mha_mlp module¶

metaperceptron.core.traditional_mlp module¶

metaperceptron.helpers package¶

metaperceptron.helpers.act_util module¶

metaperceptron.helpers.metric_util module¶

metaperceptron.helpers.preprocessor module¶

Parameters:¶

Parameters:¶

Returns:¶

Parameters:¶

Returns:¶

metaperceptron.helpers.scaler_util module¶

metaperceptron.helpers.validator module¶

Citation Request¶

Important links¶

License¶

Indices and tables¶