.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "_examples\plotly\plot_main05_boxplot_sepsis_shap.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        Click :ref:`here <sphx_glr_download__examples_plotly_plot_main05_boxplot_sepsis_shap.py>`
        to download the full example code

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

.. _sphx_glr__examples_plotly_plot_main05_boxplot_sepsis_shap.py:


05. Sepsis shap  by timestep (boxplot)
======================================

This script visualizes SHAP (SHapley Additive exPlanations) values to
analyze feature importance across different time periods. It loads a
pre-computed shap.csv file obtained for sepsis patients and a LSTM
predictive model and uses Plotly to generate a comprehensive boxplot.

The resulting plot displays the distribution of SHAP values on the y-axis
for each feature at every timestep on the x-axis. This makes it easy to
quickly identify which features have the most significant impact on the
model's predictions (those with SHAP values of higher magnitude) and
to see how that influence changes over time.

.. GENERATED FROM PYTHON SOURCE LINES 17-131



.. raw:: html
    :file: images\sphx_glr_plot_main05_boxplot_sepsis_shap_001.html





.. code-block:: default
   :lineno-start: 18


    # Libraries
    import pandas as pd
    import numpy as np
    import matplotlib as mpl
    import plotly.express as px

    from plotly.io import show
    from plotly.colors import n_colors
    from plotly.express.colors import sample_colorscale

    # See https://matplotlib.org/devdocs/users/explain/customizing.html
    mpl.rcParams['axes.titlesize'] = 8
    mpl.rcParams['axes.labelsize'] = 8
    mpl.rcParams['xtick.labelsize'] = 8
    mpl.rcParams['ytick.labelsize'] = 8
    mpl.rcParams['legend.fontsize'] = 7
    mpl.rcParams['legend.handlelength'] = 1
    mpl.rcParams['legend.handleheight'] = 1
    mpl.rcParams['legend.loc'] = 'upper left'

    try:
        __file__
        TERMINAL = True
    except:
        TERMINAL = False


    # Load data
    data = pd.read_csv('../../datasets/shap/shap.csv')

    # Show
    if TERMINAL:
        print("\nData:")
        print(data)
    data

    # Number of colors
    N = data.features.nunique()

    # see https://plotly.com/python/builtin-colorscales/#discrete-color-sequences
    # see https://plotly.github.io/plotly.py-docs/generated/plotly.express.box.html

    # generate an array of rainbow colors by fixing the saturation and lightness of the
    # HSL representation of colour and marching around the hue. Plotly accepts any CSS
    # color format, see e.g. http://www.w3schools.com/cssref/css_colors_legal.asp.
    c0 = ['hsl('+str(h)+',50%'+',50%)'
        for h in np.linspace(0, 360, N)]

    # More colors
    x = np.linspace(0, 1, N)
    c1 = sample_colorscale('viridis', list(x))
    c2 = sample_colorscale('RdBu', list(x))
    c3 = sample_colorscale('Jet', list(x))
    c4 = sample_colorscale('Agsunset', list(x))

    # .. note:: Remove width and size if running locally.

    # Boxplot
    fig = px.box(data, x='timestep', y='shap_values',
                 color='features', color_discrete_sequence=c4,
                 points='outliers', width=750, height=900)

    # .. note:: If using widescreen, commenting the legend section
    #           will automatically generate a vertical legend with
    #           scrolling if needed. For display purposes in the
    #           docs we have included the legend on top.

    # Update layout
    fig.update_layout(
        #margin={
        #    'l': 0,
        #    'r': 0,
        #    'b': 0,
        #    't': 0,
        #    'pad': 4
        #},
        legend=dict(
            orientation="h",
            entrywidth=140,
            yanchor="bottom",
            y=1.02,
            xanchor="right",
            x=1,
            #font=dict(
            #    family="Courier",
            #    size=7,
            #    #color="black"
            #),
        ),
        paper_bgcolor='rgba(0,0,0,0)',  # transparent
        plot_bgcolor='rgba(0,0,0,0)'  # transparent
    )

    # Update xaxis
    fig.update_xaxes(
        mirror=False,
        ticks='outside',
        showline=False,
        linecolor='black',
        gridcolor='lightgrey'
    )

    # Update yaxis
    fig.update_yaxes(
        mirror=False,
        ticks='outside',
        showline=False,
        linecolor='black',
        gridcolor='lightgrey'
    )

    # Show
    show(fig)


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

   **Total running time of the script:** ( 0 minutes  3.995 seconds)


.. _sphx_glr_download__examples_plotly_plot_main05_boxplot_sepsis_shap.py:


.. only :: html

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



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

     :download:`Download Python source code: plot_main05_boxplot_sepsis_shap.py <plot_main05_boxplot_sepsis_shap.py>`



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

     :download:`Download Jupyter notebook: plot_main05_boxplot_sepsis_shap.ipynb <plot_main05_boxplot_sepsis_shap.ipynb>`


.. only:: html

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

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