.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "_examples\tutorial\metrics\plot_metrics_scores.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

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

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

.. _sphx_glr__examples_tutorial_metrics_plot_metrics_scores.py:


Metrics - errors
================

Example with the 'errors' or scores implemented.

.. GENERATED FROM PYTHON SOURCE LINES 7-54




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

 .. code-block:: none


    Scores:
    -------
    Mean Absolute Error            : 0.857
    Mean Absolute Error (_)        : 0.857
    Mean Squared Error             : 1.429
    Mean Squared Log Error         : 0.006
    Mean Absolute Percentage (_)   : 6.355
    Mean Directional Accuracy (_)  : 0.143
    Mean Absolute Scaled error (_) : 1.350
    Median Absolute Error          : 1.000






|

.. code-block:: default
   :lineno-start: 8


    # General libraries.
    import numpy as np

    # Sklearn
    from sklearn.metrics import mean_absolute_error
    from sklearn.metrics import mean_squared_error
    from sklearn.metrics import mean_squared_log_error
    from sklearn.metrics import median_absolute_error

    # pyAMR
    from pyamr.metrics.scores import _mean_absolute_error
    from pyamr.metrics.scores import _mean_absolute_percentage_error
    from pyamr.metrics.scores import _mean_directional_accuracy
    from pyamr.metrics.scores import _mean_absolute_scaled_error

    # Numpy configuration
    np.set_printoptions(precision=2)

    # ---------------------------
    # create data
    # ---------------------------
    # Create time series
    y_true = np.array([10, 11, 12, 11, 12, 11, 13, 12, 13, 14, 15, 16, 18, 17])
    y_pred = np.array([11, 11, 11, 11, 12, 12, 12, 13, 13, 13, 18, 18, 18, 18])

    # Scores
    s1 = mean_absolute_error(y_true, y_pred)
    s2 = _mean_absolute_error(y_true, y_pred)
    s3 = mean_squared_error(y_true, y_pred)
    s4 = mean_squared_log_error(y_true, y_pred)
    s5 = _mean_absolute_percentage_error(y_true, y_pred)
    s6 = _mean_directional_accuracy(y_true, y_pred)
    s7 = _mean_absolute_scaled_error(y_true[:10], y_true[10:], y_pred[10:])
    s10 = median_absolute_error(y_true, y_pred)

    # Compute scores
    print("\nScores:")
    print("-------")
    print('Mean Absolute Error            : %.3f' % s1)
    print('Mean Absolute Error (_)        : %.3f' % s2)
    print('Mean Squared Error             : %.3f' % s3)
    print('Mean Squared Log Error         : %.3f' % s4)
    print('Mean Absolute Percentage (_)   : %.3f' % s5)
    print('Mean Directional Accuracy (_)  : %.3f' % s6)
    print('Mean Absolute Scaled error (_) : %.3f' % s7)
    print('Median Absolute Error          : %.3f' % s10)


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

   **Total running time of the script:** ( 0 minutes  0.003 seconds)


.. _sphx_glr_download__examples_tutorial_metrics_plot_metrics_scores.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_metrics_scores.py <plot_metrics_scores.py>`

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

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


.. only:: html

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

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