.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_4_example_dwgcs.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_plot_4_example_dwgcs.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_plot_4_example_dwgcs.py:


.. _exdwgcs:

Example: Use of DWGCS (Double-Weighting General Covariate Shift) for Covariate Shift Adaptation
============================================

Example of using DWGCS with the sythetic dataset used on the experiments 
of the corresponding paper. We load the dataset, train the DWGCS model using
data collected from training and testing distribution and predict the new instances
from the testing distribution.

.. GENERATED FROM PYTHON SOURCE LINES 15-95

.. code-block:: default


    import numpy as np
    import pandas as pd
    import time

    from MRCpy import DWGCS
    from MRCpy import CMRC
    # Import the datasets
    from MRCpy.datasets import *
    import matplotlib.pyplot as plt
    import seaborn as sns

    # Data sets
    loaders = [load_comp_vs_sci, load_comp_vs_talk, load_rec_vs_sci,
              load_rec_vs_talk, load_sci_vs_talk]
    dataName = ["comp-vs-sci", "comp-vs-talk", "rec-vs-sci",
               "rec-vs-talk", "sci-vs-talk"]
    sigma = np.array([23.5628, 23.4890, 24.5642, 25.1129, 24.8320])

    rep = 3
    n = 1000
    t = 1000

    columns = ['dataset', 'iteration', 'method', 'error']
    results = pd.DataFrame(columns=columns)

    for j, load in enumerate(loaders):

        # Loading the dataset
        X_TrainSet, Y_TrainSet, X_TestSet, Y_TestSet = load()

        TrainSet = np.concatenate((X_TrainSet, np.reshape(Y_TrainSet,(Y_TrainSet.shape[0], 1))), axis=1)
        TestSet = np.concatenate((X_TestSet, np.reshape(Y_TestSet, (Y_TestSet.shape[0], 1))), axis=1)
    
        for i in range(rep):
        
            np.random.seed(42)
            np.random.shuffle(TrainSet)
            np.random.seed(42)
            np.random.shuffle(TestSet)

            X_train = TrainSet[:n, :-1]
            Y_train = TrainSet[:n, -1]
            X_test  = TestSet[:t, :-1]
            Y_test  = TestSet[:t, -1]

            #CMRC
            clf = CMRC(loss = '0-1', phi = 'linear', fit_intercept = False, s = 0)
            clf.fit(X_train, Y_train, X_test)
            Error1 = clf.error(X_test, Y_test)

            #DWGCS D = 4
            clf2 = DWGCS(loss = '0-1', phi = 'linear', sigma_ = sigma[j], D = 1)
            clf2.fit(X_train, Y_train, X_test)
            Error2 = clf2.error(X_test, Y_test)

            #DWGCS D = 4
            clf3 = DWGCS(loss = '0-1', phi = 'linear', sigma_ = sigma[j])
            clf3.fit(X_train, Y_train, X_test)
            Error3 = clf3.error(X_test, Y_test)

    
            new_row = {'dataset': dataName[j],
                       'iteration' : i,
                       'method' : '\'CMRC\'',
                       'error': Error1}
            results.loc[len(results)] = new_row

            new_row = {'dataset': dataName[j],
                       'iteration' : i,
                       'method' : '\'DWGCS\' D = 1',
                       'error': Error2}
            results.loc[len(results)] = new_row

            new_row = {'dataset': dataName[j],
                       'iteration' : i,
                       'method' : '\'DWGCS\'',
                       'error': Error3}
            results.loc[len(results)] = new_row





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Set parameter Username
    Academic license - for non-commercial use only - expires 2024-04-06




.. GENERATED FROM PYTHON SOURCE LINES 96-104

.. code-block:: default

    sns.boxplot(x = results['dataset'], 
                y = results['error'], 
                hue = results['method'],
                palette={'\'CMRC\'' : '#ecb500', '\'DWGCS\' D = 1' : 'red', '\'DWGCS\'' : 'green'},
                width=0.5)
    plt.xlabel("Dataset")
    plt.ylabel("Classification error")
    plt.show()



.. image-sg:: /auto_examples/images/sphx_glr_plot_4_example_dwgcs_001.png
   :alt: plot 4 example dwgcs
   :srcset: /auto_examples/images/sphx_glr_plot_4_example_dwgcs_001.png
   :class: sphx-glr-single-img






.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 25 minutes  54.211 seconds)


.. _sphx_glr_download_auto_examples_plot_4_example_dwgcs.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example




    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_4_example_dwgcs.py <plot_4_example_dwgcs.py>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_4_example_dwgcs.ipynb <plot_4_example_dwgcs.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_