============
Installation
============

* Install the `current PyPI release <https://pypi.python.org/pypi/evorbf />`_::

   $ 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

.. code-block:: python

    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.

.. toctree::
   :maxdepth: 4

.. toctree::
   :maxdepth: 4

.. toctree::
   :maxdepth: 4