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

    Click :ref:`here <sphx_glr_download_advanced_mathematical_optimization_auto_examples_plot_compare_optimizers.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_advanced_mathematical_optimization_auto_examples_plot_compare_optimizers.py:


Plotting the comparison of optimizers
======================================

Plots the results from the comparison of optimizers.





.. image:: /advanced/mathematical_optimization/auto_examples/images/sphx_glr_plot_compare_optimizers_001.png
    :class: sphx-glr-single-img





.. code-block:: python


    import pickle
    import sys

    import numpy as np
    import matplotlib.pyplot as plt

    results = pickle.load(open(
        'helper/compare_optimizers_py%s.pkl' % sys.version_info[0],
        'rb'))
    n_methods = len(list(results.values())[0]['Rosenbrock  '])
    n_dims = len(results)

    symbols = 'o>*Ds'

    plt.figure(1, figsize=(10, 4))
    plt.clf()

    colors = plt.cm.nipy_spectral(np.linspace(0, 1, n_dims))[:, :3]

    method_names = list(list(results.values())[0]['Rosenbrock  '].keys())
    method_names.sort(key=lambda x: x[::-1], reverse=True)

    for n_dim_index, ((n_dim, n_dim_bench), color) in enumerate(
                zip(sorted(results.items()), colors)):
        for (cost_name, cost_bench), symbol in zip(sorted(n_dim_bench.items()),
                        symbols):
            for method_index, method_name, in enumerate(method_names):
                this_bench = cost_bench[method_name]
                bench = np.mean(this_bench)
                plt.semilogy([method_index + .1*n_dim_index, ], [bench, ],
                        marker=symbol, color=color)

    # Create a legend for the problem type
    for cost_name, symbol in zip(sorted(n_dim_bench.keys()),
                symbols):
        plt.semilogy([-10, ], [0, ], symbol, color='.5',
                label=cost_name)

    plt.xticks(np.arange(n_methods), method_names, size=11)
    plt.xlim(-.2, n_methods - .5)
    plt.legend(loc='best', numpoints=1, handletextpad=0, prop=dict(size=12),
              frameon=False)
    plt.ylabel('# function calls (a.u.)')

    # Create a second legend for the problem dimensionality
    plt.twinx()

    for n_dim, color in zip(sorted(results.keys()), colors):
        plt.plot([-10, ], [0, ], 'o', color=color,
                label='# dim: %i' % n_dim)
    plt.legend(loc=(.47, .07), numpoints=1, handletextpad=0, prop=dict(size=12),
              frameon=False, ncol=2)
    plt.xlim(-.2, n_methods - .5)

    plt.xticks(np.arange(n_methods), method_names)
    plt.yticks(())

    plt.tight_layout()
    plt.show()



**Total running time of the script:** ( 0 minutes  0.612 seconds)


.. _sphx_glr_download_advanced_mathematical_optimization_auto_examples_plot_compare_optimizers.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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