Installation¶

Install the current PyPI release:
```
$ pip install evorbf==2.1.0
```

Install directly from source code:

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

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

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

$ python
>>> import evorbf
>>> evorbf.__version__

Examples¶

In this section, we will explore the usage of the EvoRBF 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

import numpy as np
from evorbf import Data, NiaRbfRegressor
from sklearn.datasets import load_diabetes

## Load data object
# total samples = 442, total features = 10
X, y = load_diabetes(return_X_y=True)
data = Data(X, y)

## Split train and test
data.split_train_test(test_size=0.2, random_state=2)
print(data.X_train.shape, data.X_test.shape)

## Scaling dataset
data.X_train, scaler_X = data.scale(data.X_train, scaling_methods=("standard"))
data.X_test = scaler_X.transform(data.X_test)

data.y_train, scaler_y = data.scale(data.y_train, scaling_methods=("standard", ))
data.y_test = scaler_y.transform(np.reshape(data.y_test, (-1, 1)))

## Create model
opt_paras = {"name": "WOA", "epoch": 500, "pop_size": 20}
model = NiaRbfRegressor(size_hidden=25, center_finder="kmeans", regularization=False, obj_name="MSE",
                        optim="BaseGA", optim_params=opt_paras, verbose=True, seed=42)

## Train the model
model.fit(data.X_train, data.y_train)

## Test the model
y_pred = model.predict(data.X_test)

print(model.optimizer.g_best.solution)
## 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_metrics=["R2", "R", "KGE", "MAPE"]))
print(model.evaluate(y_true=data.y_test, y_pred=y_pred, list_metrics=["MSE", "RMSE", "R2S", "NSE", "KGE", "MAPE"]))

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.