Metadata-Version: 2.1
Name: evocov
Version: 0.0.6
Summary: EvoCov extension to learn the kernel function.
Home-page: https://gitlab.com/ibaidev/evocov
Author: Ibai Roman
Author-email: ibaidev@protonmail.com
License: GPLv3
Keywords: Kernel Learning
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: POSIX
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Dist: gplib
Requires-Dist: numpy
Requires-Dist: deap


EvoCov
======

GPlib extension to learn the kernel function.

Setup evocov
------------

- Create and activate virtualenv (for python2) or
  venv (for python3)

.. code-block:: bash

  # for python3
  python3 -m venv .env
  # or for python2
  python2 -m virtualenv .env

  source .env/bin/activate

- Upgrade pip

.. code-block:: bash

  python -m pip install --upgrade pip

- Install EvoCov package

.. code-block:: bash

  python -m pip install evocov


Use EvoCov
----------------------

- Import EvoCov and GPlib to use it in your python script.

.. code-block:: python

  import gplib
  import evocov

- Initialize the GP with None covariance function.

.. code-block:: python

  gp = gplib.GP(
      mean_function=gplib.mea.Fixed(),
      covariance_function=gplib.ker.WhiteNoise()
  )

- Configure the fitting method.

.. code-block:: python

  lml = gplib.me.LML()
  bic = gplib.me.BIC()

  fitting_method = evocov.fit.EvoCov(
      obj_fun=bic.fold_measure,
      max_fun_call=25000,
      nested_fit_method=gplib.fit.MultiStart(
          obj_fun=lml.fold_measure,
          max_fun_call=250,
          nested_fit_method=gplib.fit.LocalSearch(
              obj_fun=lml.fold_measure,
              method="Powell",
              max_fun_call=100
          )
      )
  )

- Generate some random data.

.. code-block:: python

  import numpy as np
  data = {
    'X': np.arange(3, 8, 1.)[:, None],
    'Y': np.random.uniform(0, 2, 5)[:, None]
  }

- Fit the kernel and the hyperparameters to the training set.

.. code-block:: python

  validation = gplib.dm.Full()

  log = fitting_method.fit(gp, validation.get_folds(
      data
  ))

- Finally plot the posterior GP.

.. code-block:: python

  posterior_gp = gp.get_posterior(data)
  gplib.plot.gp_1d(posterior_gp, data, n_samples=10)

- There are more examples in examples/ directory. Check them out!

Develop EvoCov
--------------

-  Download the repository using git

.. code-block:: bash

  git clone https://gitlab.com/ibaidev/evocov.git
  cd evocov
  git config user.email 'MAIL'
  git config user.name 'NAME'
  git config credential.helper 'cache --timeout=300'
  git config push.default simple

-  Update API documentation

.. code-block:: bash

  source ./.env/bin/activate
  pip install Sphinx
  cd docs/
  sphinx-apidoc -f -o ./ ../evocov


