.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "recipes/004_visualization.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_recipes_004_visualization.py>`
        to download the full example code.

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

.. _sphx_glr_recipes_004_visualization.py:


.. _visualization:

How to Implement Your Own Visualization Function
===========================================================

In this section, we show how to implement your own visualization function.
Here, we implement a visualization function that plots the optimization history.

.. GENERATED FROM PYTHON SOURCE LINES 12-13

First of all, import `optuna` and other required modules.

.. GENERATED FROM PYTHON SOURCE LINES 13-22

.. code-block:: Python


    from __future__ import annotations

    import matplotlib
    import matplotlib.pyplot as plt
    import optuna
    import plotly.graph_objects as go









.. GENERATED FROM PYTHON SOURCE LINES 23-24

If you use `plotly <https://plotly.com/python/>`__ for visualization, the function should return a ``plotly.graph_objects.Figure`` object.

.. GENERATED FROM PYTHON SOURCE LINES 24-44

.. code-block:: Python



    def plot_optimizaiton_history(study: optuna.study.Study) -> go.Figure:
        trials = study.trials
        values = [trial.value for trial in trials]
        best_values = [min(values[: i + 1]) for i in range(len(values))]  # type: ignore
        iterations = list(range(len(trials)))

        fig = go.Figure()
        fig.add_trace(
            go.Scatter(
                x=iterations,
                y=best_values,
                mode="lines+markers",
            )
        )
        fig.update_layout(title="Optimization history")
        return fig









.. GENERATED FROM PYTHON SOURCE LINES 45-46

If you use `matplotlib <https://matplotlib.org/>`__ for visualization, the function should return a ``matplotlib.figure.Figure`` object.

.. GENERATED FROM PYTHON SOURCE LINES 46-59

.. code-block:: Python



    def plot_optimizaiton_history_matplotlib(study: optuna.study.Study) -> matplotlib.figure.Figure:
        trials = study.trials
        values = [trial.value for trial in trials]
        best_values = [min(values[: i + 1]) for i in range(len(values))]  # type: ignore

        fig, ax = plt.subplots()
        ax.set_title("Optimization history")
        ax.plot(best_values, marker="o")
        return fig









.. GENERATED FROM PYTHON SOURCE LINES 60-62

Plot the optimization history using the implemented visualization function.
Here, we use the simple quadratic function as objective function.

.. GENERATED FROM PYTHON SOURCE LINES 62-76

.. code-block:: Python



    def objective(trial: optuna.trial.Trial) -> float:
        x = trial.suggest_float("x", -10, 10)
        return x**2


    study = optuna.create_study()
    study.optimize(objective, n_trials=100)

    fig = plot_optimizaiton_history(study)
    fig.show()  # plt.show() for matplotlib









.. GENERATED FROM PYTHON SOURCE LINES 77-79

After implementing your own visualization function, you can register it with OptunaHub.
See :doc:`002_registration` for how to register your visualization function with OptunaHub.


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

   **Total running time of the script:** (0 minutes 0.377 seconds)


.. _sphx_glr_download_recipes_004_visualization.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: 004_visualization.ipynb <004_visualization.ipynb>`

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

      :download:`Download Python source code: 004_visualization.py <004_visualization.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: 004_visualization.zip <004_visualization.zip>`


.. only:: html

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

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